paper_id string | title string | authors list | miccai_url string | pdf_url string | doi string | sharedit_url string | supplementary_url string | topics list | code_urls list | dataset_urls list | pages string | bibtex large_string | abstract large_string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Paper0308 | µ2 Tokenizer: Differentiable Multi-Scale Multi-Modal Tokenizer for Radiology Report Generation | [
"Siyou Li",
"Pengyao Qin",
"Huanan Wu",
"Dong Nie",
"Arun J. Thirunavukarasu",
"Juntao Yu",
"Le Zhang"
] | https://papers.miccai.org/miccai-2025/0001-Paper0308.html | https://papers.miccai.org/miccai-2025/paper/0308_paper.pdf | 10.1007/978-3-032-04971-1_1 | https://rdcu.be/eHwS7 | null | [
"Body -> other",
"Modalities -> CT / X-Ray",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models"
] | [
"https://github.com/Siyou-Li/u2Tokenizer"
] | [
"https://huggingface.co/datasets/SiyouLi/CT-RATE-Chinese",
"https://huggingface.co/datasets/SiyouLi/CT-RATE-Mini"
] | 3-12 | @InProceedings{LiSiy_µ2_MICCAI2025,
author = { Li, Siyou AND Qin, Pengyao AND Wu, Huanan AND Nie, Dong AND Thirunavukarasu, Arun J. AND Yu, Juntao AND Zhang, Le},
title = { { µ2 Tokenizer: Differentiable Multi-Scale Multi-Modal Tokenizer for Radiology Report Generation } },
booktitle = {proceedi... | Automated radiology report generation (RRG) aims to produce detailed textual reports from clinical imaging, such as computed tomography (CT) scans, to improve the accuracy and efficiency of diagnosis and provision of management advice. RRG is complicated by two key challenges: (1) inherent complexity in extracting rele... | 2507.00316 | title_snapshot |
Paper1272 | 3D Acetabular Surface Reconstruction from 2D Pre-operative X-ray Images using SRVF Elastic Registration and Deformation Graph | [
"Shuai Zhang",
"Jinliang Wang",
"Xu Wang",
"Sujith Konan",
"Danail Stoyanov",
"Evangelos B. Mazomenos"
] | https://papers.miccai.org/miccai-2025/0002-Paper1272.html | https://papers.miccai.org/miccai-2025/paper/1272_paper.pdf | 10.1007/978-3-032-05325-1_1 | https://rdcu.be/eHxdV | null | [
"Body -> other",
"Body -> Spine",
"Modalities -> CT / X-Ray",
"Modalities -> Robotics",
"Applications -> Image Registration",
"Applications -> Image-Guided Interventions",
"Surgery -> Planning and Simulation"
] | [
"https://github.com/zsustc/3D-ASR"
] | [
"https://github.com/zsustc/3D-ASR"
] | 3-12 | @InProceedings{ZhaShu_3D_MICCAI2025,
author = { Zhang, Shuai AND Wang, Jinliang AND Wang, Xu AND Konan, Sujith AND Stoyanov, Danail AND Mazomenos, Evangelos B.},
title = { { 3D Acetabular Surface Reconstruction from 2D Pre-operative X-ray Images using SRVF Elastic Registration and Deformation Graph } },... | Accurate and reliable selection of the appropriate acetabular cup size is crucial for restoring joint biomechanics in total hip arthroplasty (THA). This paper proposes a novel framework integrating square-root velocity function (SRVF)-based elastic shape registration technique with an embedded deformation (ED) graph ap... | 2503.22177 | title_snapshot |
Paper2258 | 3D Dynamic Prediction of Missing Teeth in Diverse Patterns via Centroid-prompted Diffusion Model | [
"Zongrui Ji",
"Na Li",
"Peng Xue",
"Yi Dong",
"Lei Ma"
] | https://papers.miccai.org/miccai-2025/0003-Paper2258.html | https://papers.miccai.org/miccai-2025/paper/2258_paper.pdf | 10.1007/978-3-032-05114-1_1 | https://rdcu.be/eHw0P | null | [
"Body -> other",
"Modalities -> CT / X-Ray",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning",
"Surgery -> Mixed / Augmented / Virtual Reality",
"Surgery -> Navigation",
"Surgery -> Planning and Simulation"
] | [] | [] | 3-12 | @InProceedings{JiZon_3D_MICCAI2025,
author = { Ji, Zongrui AND Li, Na AND Xue, Peng AND Dong, Yi AND Ma, Lei},
title = { { 3D Dynamic Prediction of Missing Teeth in Diverse Patterns via Centroid-prompted Diffusion Model } },
booktitle = {proceedings of Medical Image Computing and Computer Assist... | Dental implantation restores missing teeth through surgical insertion of artificial roots, relying on preoperative digital planning to ensure precision and efficiency. However, critical challenges persist in virtual tooth positioning: this process demands extensive clinical expertise and time-consuming manual adjustmen... | null | null |
Paper2701 | 4D CardioSynth: Synthesising Dynamic Virtual Heart Populations through Spatiotemporal Disentanglement | [
"Haoran Dou",
"Jinghan Huang",
"Arezoo Zakeri",
"Zherui Zhou",
"Tingting Mu",
"Jinming Duan",
"Alejandro F. Frangi"
] | https://papers.miccai.org/miccai-2025/0004-Paper2701.html | https://papers.miccai.org/miccai-2025/paper/2701_paper.pdf | 10.1007/978-3-032-04947-6_1 | https://rdcu.be/eHwPn | null | [
"Body -> Cardiac",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [] | [] | 3-12 | @InProceedings{DouHao_4D_MICCAI2025,
author = { Dou, Haoran AND Huang, Jinghan AND Zakeri, Arezoo AND Zhou, Zherui AND Mu, Tingting AND Duan, Jinming AND Frangi, Alejandro F.},
title = { { 4D CardioSynth: Synthesising Dynamic Virtual Heart Populations through Spatiotemporal Disentanglement } },
... | Dynamic virtual populations are critical for realistic in-silico cardiovascular trials, yet current approaches primarily generate static anatomies, limiting their clinical and computational value. In this study, we present 4D CardioSynth, a generative framework for constructing dynamic 3D virtual populations of cardiov... | null | null |
Paper4160 | 6D Object Pose Tracking for Orthopedic Surgical Training using Visual-Inertial Sensor Fusion | [
"Maarten Hogenkamp",
"Tobias Stauffer",
"Quentin Lohmeyer",
"Mirko Meboldt"
] | https://papers.miccai.org/miccai-2025/0005-Paper4160.html | https://papers.miccai.org/miccai-2025/paper/4160_paper.pdf | 10.1007/978-3-032-05114-1_2 | https://rdcu.be/eHw0S | null | [
"Body -> other",
"Body -> Spine",
"Modalities -> Other",
"Modalities -> Photograph / Video",
"Applications -> Image-Guided Interventions",
"Surgery -> Navigation",
"Surgery -> Planning and Simulation",
"Surgery -> Scene Understanding",
"Surgery -> Skill and Work Flow Analysis"
] | [
"https://github.com/MountainCoot/fusionpose"
] | [] | 13-23 | @InProceedings{HogMaa_6D_MICCAI2025,
author = { Hogenkamp, Maarten AND Stauffer, Tobias AND Lohmeyer, Quentin AND Meboldt, Mirko},
title = { { 6D Object Pose Tracking for Orthopedic Surgical Training using Visual-Inertial Sensor Fusion } },
booktitle = {proceedings of Medical Image Computing and... | Digital training simulators play a growing role in orthopedic surgery, offering realistic, standardized, and risk-free learning environments without the need for constant expert supervision. To enable simulators with realistic tactile feedback and haptic sensations, accurate tracking of surgical tools and anatomical st... | null | null |
Paper0977 | A Boundary-aware Cold-Diffusion Model for Electron Microscopy Segmentation | [
"Muge Qi",
"Ruohua Shi",
"Yu Cai",
"Liuyuan He",
"Wenyao Wang",
"Lei Ma"
] | https://papers.miccai.org/miccai-2025/0006-Paper0977.html | https://papers.miccai.org/miccai-2025/paper/0977_paper.pdf | 10.1007/978-3-032-05325-1_2 | https://rdcu.be/eHxdW | null | [
"Modalities -> Microscopy",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [] | [] | 13-23 | @InProceedings{QiMug_ABoundaryaware_MICCAI2025,
author = { Qi, Muge AND Shi, Ruohua AND Cai, Yu AND He, Liuyuan AND Wang, Wenyao AND Ma, Lei},
title = { { A Boundary-aware Cold-Diffusion Model for Electron Microscopy Segmentation } },
booktitle = {proceedings of Medical Image Computing and Compu... | The advancement of electron microscopy (EM) imaging technology has expanded its applications in life science research, making the automation of EM image analysis a key focus in biomedical imaging. As a core task in EM image analysis, semantic segmentation has garnered significant attention, and convolutional neural net... | null | null |
Paper0169 | A Causal-holistic Adaptive Intervention Network for Tailoring Automated Coronary Artery Disease Diagnosis to Individual Patients | [
"Xinghua Ma",
"Xingyu Qiu",
"Yuetan Chu",
"Kuanquan Wang",
"Zhaowen Qiu",
"Gongning Luo",
"Xin Gao"
] | https://papers.miccai.org/miccai-2025/0007-Paper0169.html | https://papers.miccai.org/miccai-2025/paper/0169_paper.pdf | 10.1007/978-3-032-04984-1_1 | https://rdcu.be/eHwX5 | null | [
"Body -> Vasculature",
"Modalities -> Other",
"Applications -> Computer Aided Diagnosis"
] | [] | [] | 3-13 | @InProceedings{MaXin_ACausalholistic_MICCAI2025,
author = { Ma, Xinghua AND Qiu, Xingyu AND Chu, Yuetan AND Wang, Kuanquan AND Qiu, Zhaowen AND Luo, Gongning AND Gao, Xin},
title = { { A Causal-holistic Adaptive Intervention Network for Tailoring Automated Coronary Artery Disease Diagnosis to Individual... | Given the global prevalence and high mortality of coronary artery disease (CAD), automated CAD diagnosis should evolve toward personalized methods to maximize its clinical value. However, existing techniques have been limited to artery-level prediction, lacking patient-level causality and failing to effectively account... | null | null |
Paper1017 | A Causality-Inspired Model for Intima-Media Thickening Assessment in Ultrasound Videos | [
"Shuo Gao",
"Meng Yang",
"Jun Xue",
"Yang Chen",
"Jingyang Zhang",
"Guangquan Zhou"
] | https://papers.miccai.org/miccai-2025/0008-Paper1017.html | https://papers.miccai.org/miccai-2025/paper/1017_paper.pdf | 10.1007/978-3-032-04984-1_2 | https://rdcu.be/eHwX6 | null | [
"Body -> Vasculature",
"Modalities -> Ultrasound",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Interpretability / Explainability"
] | [
"https://github.com/xielaobanyy/causal-imt"
] | [] | 14-23 | @InProceedings{GaoShu_ACausalityInspired_MICCAI2025,
author = { Gao, Shuo AND Yang, Meng AND Xue, Jun AND Chen, Yang AND Zhang, Jingyang AND Zhou, Guangquan},
title = { { A Causality-Inspired Model for Intima-Media Thickening Assessment in Ultrasound Videos } },
booktitle = {proceedings of Medic... | Carotid atherosclerosis represents a significant health risk, with its early diagnosis primarily dependent on ultrasound-based assessments of carotid intima-media thickening. However, during carotid ultrasound screening, significant view variations cause style shifts, impairing content cues related to thickening, such ... | 2503.12418 | title_snapshot |
Paper2655 | A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images | [
"Yifan Gao",
"Shaohao Rui",
"Haoyang Su",
"Jinyi Xiang",
"Lianming Wu",
"Xiaosong Wang"
] | https://papers.miccai.org/miccai-2025/0009-Paper2655.html | https://papers.miccai.org/miccai-2025/paper/2655_paper.pdf | 10.1007/978-3-032-04927-8_1 | https://rdcu.be/eHwKL | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [] | [] | 3-13 | @InProceedings{GaoYif_AComposite_MICCAI2025,
author = { Gao, Yifan AND Rui, Shaohao AND Su, Haoyang AND Xiang, Jinyi AND Wu, Lianming AND Wang, Xiaosong},
title = { { A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images } },
booktitle = {proceedin... | Accurate segmentation of myocardial lesions from multi-sequence cardiac magnetic resonance imaging is essential for cardiac disease diagnosis and treatment planning. However, achieving optimal feature correspondence is challenging due to intensity variations across modalities and spatial misalignment caused by inconsis... | 2507.11886 | title_snapshot |
Paper1603 | A Curvature-Guided Diffeomorphic Mesh Deformation Framework for Lifespan Brain Cortical Surface Reconstruction | [
"Lin Teng",
"Shen Zhao",
"Feng Shi",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2025/0010-Paper1603.html | https://papers.miccai.org/miccai-2025/paper/1603_paper.pdf | 10.1007/978-3-032-04927-8_2 | https://rdcu.be/eHwKM | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Computational (Integrative) Pathology",
"Applications -> Computational Anatomy and Physiology",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Surgery -> Other",
"Special Topic -> Low-Cost and Point-of-Care Imagi... | [] | [] | 14-23 | @InProceedings{TenLin_ACurvatureGuided_MICCAI2025,
author = { Teng, Lin AND Zhao, Shen AND Shi, Feng AND Shen, Dinggang},
title = { { A Curvature-Guided Diffeomorphic Mesh Deformation Framework for Lifespan Brain Cortical Surface Reconstruction } },
booktitle = {proceedings of Medical Image Comp... | Accurate and automatic lifespan brain cortical surface reconstruction (CSR) is crucial for analyzing brain development and aging. Traditional pipelines involve multiple processing steps, which are time-intensive and inefficient for handling larger datasets. While deep learning-based methods can accelerate reconstructio... | null | null |
Paper2515 | A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging | [
"Xuanru Zhou",
"Jiarun Liu",
"Shoujun Yu",
"Hao Yang",
"Cheng Li",
"Tao Tan",
"Shanshan Wang"
] | https://papers.miccai.org/miccai-2025/0011-Paper2515.html | https://papers.miccai.org/miccai-2025/paper/2515_paper.pdf | 10.1007/978-3-032-05127-1_1 | https://rdcu.be/eHw28 | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning",
"Surgery -> Data Science",
"Special Topic -> Low-Cost and Point-of-Care Imaging Solutions"
] | [
"https://github.com/Joker-ZXR/TSSC-Net"
] | [] | 3-12 | @InProceedings{ZhoXua_ADiffusionDriven_MICCAI2025,
author = { Zhou, Xuanru AND Liu, Jiarun AND Yu, Shoujun AND Yang, Hao AND Li, Cheng AND Tan, Tao AND Wang, Shanshan},
title = { { A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging } },
... | In medical imaging, 4D MRI enables dynamic 3D visualization, yet the trade-off between spatial and temporal resolution requires prolonged scan time that can compromise temporal fidelity—especially during rapid, large-amplitude motion. Traditional approaches typically rely on registration-based interpolation to generate... | 2506.04116 | title_snapshot |
Paper1381 | A flexible deep learning framework for survival analysis with medical data | [
"Gabriele Campanella",
"Ida Häggström",
"Lucas Kook",
"Torsten Hothorn",
"Thomas J. Fuchs"
] | https://papers.miccai.org/miccai-2025/0012-Paper1381.html | https://papers.miccai.org/miccai-2025/paper/1381_paper.pdf | 10.1007/978-3-032-05182-0_1 | https://rdcu.be/eG4CR | null | [
"Body -> Breast",
"Body -> Lung",
"Body -> other",
"Modalities -> CT / X-Ray",
"Modalities -> Other",
"Applications -> Computational (Integrative) Pathology",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning... | [
"https://github.com/sinai-computational-pathology/DCTM"
] | [
"https://www.cancer.gov/ccg/research/genome-sequencing/tcga",
"https://cancerimagingarchive.net/collection/radcure",
"https://github.com/jaredleekatzman/DeepSurv"
] | 3-13 | @InProceedings{CamGab_Aflexible_MICCAI2025,
author = { Campanella, Gabriele AND Häggström, Ida AND Kook, Lucas AND Hothorn, Torsten AND Fuchs, Thomas J.},
title = { { A flexible deep learning framework for survival analysis with medical data } },
booktitle = {proceedings of Medical Image Computi... | Medical imaging data and electronic health records are an integral part of clinical routine and research for prognostication of patient survival and thus directly inform patient management. However, standard regression models used to derive patient prognoses are ill-equipped to handle such non-tabular data directly. Se... | null | null |
Paper2387 | A Frequency-Aware Self-Supervised Learning for Ultra-Wide-Field Image Enhancement | [
"Weicheng Liao",
"Zan Chen",
"Jianyang Xie",
"Yalin Zheng",
"Yuhui Ma",
"Yitian Zhao"
] | https://papers.miccai.org/miccai-2025/0013-Paper2387.html | https://papers.miccai.org/miccai-2025/paper/2387_paper.pdf | 10.1007/978-3-032-05169-1_1 | https://rdcu.be/eHw8b | null | [
"Body -> Eye",
"Applications -> Other",
"Machine Learning -> Deep Learning"
] | [] | [] | 3-13 | @InProceedings{LiaWei_AFrequencyAware_MICCAI2025,
author = { Liao, Weicheng AND Chen, Zan AND Xie, Jianyang AND Zheng, Yalin AND Ma, Yuhui AND Zhao, Yitian},
title = { { A Frequency-Aware Self-Supervised Learning for Ultra-Wide-Field Image Enhancement } },
booktitle = {proceedings of Medical Ima... | Ultra-Wide-Field (UWF) retinal imaging has revolutionized retinal diagnostics by providing a comprehensive view of the retina. However, it often suffers from quality-degrading factors such as blurring and uneven illumination, which obscure fine details and mask pathological information. While numerous retinal image enh... | 2508.19664 | title_snapshot |
Paper3193 | A Holistic Time-Aware Classification Model for Multimodal Longitudinal Patient Data | [
"Tobias Susetzky",
"Huaqi Qiu",
"Rickmer Braren",
"Daniel Rueckert"
] | https://papers.miccai.org/miccai-2025/0014-Paper3193.html | https://papers.miccai.org/miccai-2025/paper/3193_paper.pdf | 10.1007/978-3-032-04927-8_3 | https://rdcu.be/eHwKN | null | [
"Body -> other",
"Modalities -> Other",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Machine Learning -> Other"
] | [
"https://github.com/go31glX57/tamme"
] | [
"https://mimic.mit.edu"
] | 24-33 | @InProceedings{SusTob_AHolistic_MICCAI2025,
author = { Susetzky, Tobias AND Qiu, Huaqi AND Braren, Rickmer AND Rueckert, Daniel},
title = { { A Holistic Time-Aware Classification Model for Multimodal Longitudinal Patient Data } },
booktitle = {proceedings of Medical Image Computing and Computer ... | Current prognostic and diagnostic AI models for healthcare often limit informational input capacity by being time-agnostic and focusing on single modalities, therefore lacking the holistic perspective clinicians rely on. To address this, we introduce a Time-Aware MultiModal Transformer Encoder (TAMME) for longitudinal ... | null | null |
Paper3657 | A Hybrid Contrastive Ordinal Regression Method for Advancing Disease Severity Assessment in Imbalanced Medical Datasets | [
"Afsah Saleem",
"Joshua R. Lewis",
"Syed Zulqarnain Gilani"
] | https://papers.miccai.org/miccai-2025/0015-Paper3657.html | https://papers.miccai.org/miccai-2025/paper/3657_paper.pdf | 10.1007/978-3-032-05169-1_2 | https://rdcu.be/eHw8c | null | [
"Body -> Breast",
"Body -> Eye",
"Modalities -> Photograph / Video",
"Modalities -> Ultrasound",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/AfsahS/A-Hybrid-Contrastive-Ordinal-Regression"
] | [
"https://www.kaggle.com/c/diabetic-retinopathy-detection",
"https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset"
] | 14-23 | @InProceedings{SalAfs_AHybrid_MICCAI2025,
author = { Saleem, Afsah AND Lewis, Joshua R. AND Gilani, Syed Zulqarnain},
title = { { A Hybrid Contrastive Ordinal Regression Method for Advancing Disease Severity Assessment in Imbalanced Medical Datasets } },
booktitle = {proceedings of Medical Image... | Accurate disease grading is critical for early diagnosis and effective treatment planning. However, class imbalance and subtle inter- class variations in real-world disease grading datasets make it challeng- ing for traditional classification models to differentiate between neigh- boring disease stages and preserve ord... | null | null |
Paper2060 | A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation | [
"Guoshi Li",
"Pew-Thian Yap"
] | https://papers.miccai.org/miccai-2025/0016-Paper2060.html | https://papers.miccai.org/miccai-2025/paper/2060_paper.pdf | 10.1007/978-3-032-04937-7_1 | https://rdcu.be/eHwMM | null | [
"Body -> Brain",
"Modalities -> MRI - Functional MRI",
"Applications -> Brain Network Analysis",
"Applications -> Computational (Integrative) Pathology"
] | [] | [
"https://adni.loni.usc.edu/",
"https://www.humanconnectome.org/study/hcp-young-adult/data-releases"
] | 3-12 | @InProceedings{LiGuo_ALargescale_MICCAI2025,
author = { Li, Guoshi AND Yap, Pew-Thian},
title = { { A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
... | The development of a computational framework that can infer largescale brain-wide effective connectivity (EC) based on resting-state functional MRI (rs-fMRI) represents a grand challenge to computational neuroimaging. Towards the goal of estimating full-scale, whole-brain EC, we developed a new computational framework ... | null | null |
Paper4198 | A Learning Framework for Predicting CT-based PRM Biomarker from MRI Sequences in COPD | [
"Yiling Xu",
"Simon M. F. Triphan",
"Julian Grolig",
"Hanyi Zhang",
"Jürgen Biederer",
"Craig J. Galbán",
"Hans-Ulrich Kauczor",
"Mark O. Wielpütz",
"Oliver Weinheimer"
] | https://papers.miccai.org/miccai-2025/0017-Paper4198.html | https://papers.miccai.org/miccai-2025/paper/4198_paper.pdf | 10.1007/978-3-032-04984-1_3 | https://rdcu.be/eHwX7 | null | [
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Modalities -> MRI",
"Modalities -> MRI - Functional MRI",
"Applications -> Image Segmentation",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/YilingMed/MR2PRM4COPD"
] | [] | 24-33 | @InProceedings{XuYil_ALearning_MICCAI2025,
author = { Xu, Yiling AND Triphan, Simon M. F. AND Grolig, Julian AND Zhang, Hanyi AND Biederer, Jürgen AND Galbán, Craig J. AND Kauczor, Hans-Ulrich AND Wielpütz, Mark O. AND Weinheimer, Oliver},
title = { { A Learning Framework for Predicting CT-based PRM Bio... | Image-based biomarkers provide non-invasive regional assessment of structural-functional abnormalities in Chronic Obstructive Pulmonary Disease (COPD). For example, quantitative computed tomography (QCT) identifies emphysema and small airway disease, while functional MRI measures lung ventilation and perfusion. In rece... | null | null |
Paper5388 | A Model Order-Free Method for Stable States Extraction in Dynamic Functional Connectivity | [
"Songke Fang",
"Vince D. Calhoun",
"Godfrey Pearlson",
"Peter Kochunov",
"Theo G. M. van Erp",
"Yuhui Du"
] | https://papers.miccai.org/miccai-2025/0018-Paper5388.html | https://papers.miccai.org/miccai-2025/paper/5388_paper.pdf | 10.1007/978-3-032-05162-2_1 | https://rdcu.be/eHc3z | null | [
"Body -> Brain",
"Modalities -> MRI - Functional MRI",
"Applications -> Brain Network Analysis",
"Machine Learning -> Interpretability / Explainability"
] | [] | [] | 3-12 | @InProceedings{FanSon_AModel_MICCAI2025,
author = { Fang, Songke AND Calhoun, Vince D. AND Pearlson, Godfrey AND Kochunov, Peter AND van Erp, Theo G. M. AND Du, Yuhui},
title = { { A Model Order-Free Method for Stable States Extraction in Dynamic Functional Connectivity } },
booktitle = {proceed... | Dynamic functional connectivity (dFC) analysis has revealed that functional connectivity fluctuates over short timescales, reflecting the intrinsic transitions of brain among multiple states. However, dFC data typically exhibit the characteristics of high dimensionality and noise, making it difficult to extract stable ... | null | null |
Paper0782 | A Multi-Branch Framework for Cross-Domain Vessel Segmentation via the Few-Shot Paradigm | [
"Zihang Huang",
"Tianyu Zhao",
"Liang Zhang",
"Xin Yang"
] | https://papers.miccai.org/miccai-2025/0019-Paper0782.html | https://papers.miccai.org/miccai-2025/paper/0782_paper.pdf | 10.1007/978-3-032-04971-1_2 | https://rdcu.be/eHwS8 | null | [
"Body -> Vasculature",
"Modalities -> Other",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/ZiH-Huang/FSS_Cross."
] | [] | 13-23 | @InProceedings{HuaZih_AMultiBranch_MICCAI2025,
author = { Huang, Zihang AND Zhao, Tianyu AND Zhang, Liang AND Yang, Xin},
title = { { A Multi-Branch Framework for Cross-Domain Vessel Segmentation via the Few-Shot Paradigm } },
booktitle = {proceedings of Medical Image Computing and Computer Assi... | In recent years, deep learning-based vessel segmentation methods have made significant progress. However, the diversity of image modalities and the high-cost of acquiring sufficient annotated data constrain the performance of existing approaches. Given that the primary objective of segmenting various types of vessels i... | null | null |
Paper4023 | A Multimodal Contrastive Learning for Detecting Aortic Dissection on 3D Non-Contrast CT with Anatomy Simplification | [
"Duoer Zhang",
"Wenbo Xiao",
"Chen Jiang",
"Yuxuan Qiu",
"Zhan Feng",
"Hong Wang",
"Yefeng Zheng",
"Wentao Zhu"
] | https://papers.miccai.org/miccai-2025/0020-Paper4023.html | https://papers.miccai.org/miccai-2025/paper/4023_paper.pdf | 10.1007/978-3-032-04981-0_1 | https://rdcu.be/eHwVW | null | [
"Body -> Vasculature",
"Modalities -> CT / X-Ray",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [] | [] | 3-12 | @InProceedings{ZhaDuo_AMultimodal_MICCAI2025,
author = { Zhang, Duoer AND Xiao, Wenbo AND Jiang, Chen AND Qiu, Yuxuan AND Feng, Zhan AND Wang, Hong AND Zheng, Yefeng AND Zhu, Wentao},
title = { { A Multimodal Contrastive Learning for Detecting Aortic Dissection on 3D Non-Contrast CT with Anatomy Simplif... | Accurate detection of aortic dissection (AD) in emergency settings is of significant importance, as misdiagnosis can significantly delay subsequent treatments and even endanger patients’ lives. Currently, non-contrast CT scans are standard protocols in emergency departments for patients with chest pain, yet their abili... | null | null |
Paper2987 | A New Paradigm for Low-dose PET/CT Reconstruction with Mamba-powered Progressive Network and Physics-informed Consistency | [
"Zixin Tang",
"Caiwen Jiang",
"Zhiming Cui",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2025/0021-Paper2987.html | https://papers.miccai.org/miccai-2025/paper/2987_paper.pdf | 10.1007/978-3-032-05141-7_1 | https://rdcu.be/eHw5L | null | [
"Body -> Abdomen",
"Body -> other",
"Modalities -> CT / X-Ray",
"Modalities -> Nuclear Imaging",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning",
"Surgery -> Data Science",
"Special Topic -> Low-Cost and Point-of-Care Imaging Solutions"
] | [] | [
"https://www.cancerimagingarchive.net/collections/"
] | 3-12 | @InProceedings{TanZix_ANew_MICCAI2025,
author = { Tang, Zixin AND Jiang, Caiwen AND Cui, Zhiming AND Shen, Dinggang},
title = { { A New Paradigm for Low-dose PET/CT Reconstruction with Mamba-powered Progressive Network and Physics-informed Consistency } },
booktitle = {proceedings of Medical Ima... | Positron Emission Tomography (PET) is a powerful imaging technique but involves radiation exposure due to the use of radioactive tracers. A promising solution to mitigate this risk is reconstructing standard-dose PET (SPET) from low-dose PET (LPET). Previous studies have primarily focused on attenuation-corrected PET d... | null | null |
Paper4374 | A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage | [
"Youngjin Yoo",
"Bogdan Georgescu",
"Yanbo Zhang",
"Sasa Grbic",
"Han Liu",
"Gabriela D. Aldea",
"Thomas J. Re",
"Jyotipriya Das",
"Poikavila Ullaskrishnan",
"Eva Eibenberger",
"Andrei Chekkoury",
"Uttam K. Bodanapally",
"Savvas Nicolaou",
"Pina C. Sanelli",
"Thomas J. Schroeppel",
"Yv... | https://papers.miccai.org/miccai-2025/0022-Paper4374.html | https://papers.miccai.org/miccai-2025/paper/4374_paper.pdf | 10.1007/978-3-032-04965-0_1 | https://rdcu.be/eHaVh | null | [
"Body -> Brain",
"Modalities -> CT / X-Ray",
"Applications -> Anomaly Detection",
"Machine Learning -> Foundation Models"
] | [] | [] | 3-13 | @InProceedings{YooYou_ANoncontrast_MICCAI2025,
author = { Yoo, Youngjin AND Georgescu, Bogdan AND Zhang, Yanbo AND Grbic, Sasa AND Liu, Han AND Aldea, Gabriela D. AND Re, Thomas J. AND Das, Jyotipriya AND Ullaskrishnan, Poikavila AND Eibenberger, Eva AND Chekkoury, Andrei AND Bodanapally, Uttam K. AND Nicolaou,... | Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of an increasing request of such scans and a global shortage in radiologists. This study introduces a 3D foundation model for detecting divers... | 2502.21106 | title_snapshot |
Paper0666 | A Novel ED Triage Framework Using Conditional Imputation, Multi-Scale Semantic Learning, and Cross-Modal Fusion | [
"Yi Xiao",
"Jun Zhang",
"Cheng Chi",
"Chunyu Wang"
] | https://papers.miccai.org/miccai-2025/0023-Paper0666.html | https://papers.miccai.org/miccai-2025/paper/0666_paper.pdf | 10.1007/978-3-032-05127-1_2 | https://rdcu.be/eHw29 | null | [
"Body -> other",
"Modalities -> Other",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Other",
"Surgery -> Data Science"
] | [
"https://github.com/xiaoyiseu/CGMI"
] | [
"https://github.com/xiaoyiseu/CGMI/tree/main/data"
] | 13-22 | @InProceedings{XiaYi_ANovel_MICCAI2025,
author = { Xiao, Yi AND Zhang, Jun AND Chi, Cheng AND Wang, Chunyu},
title = { { A Novel ED Triage Framework Using Conditional Imputation, Multi-Scale Semantic Learning, and Cross-Modal Fusion } },
booktitle = {proceedings of Medical Image Computing and Co... | In emergency departments (ED), efficient triage is essential for timely patient care, but challenges like missing and sparse data often hinder the prediction performance of severity level and department. To address these issues, we propose a novel intelligent triage method that incorporates a Conditional Gaussian Mixtu... | null | null |
Paper0161 | A novel Fourier Adjacency Transformer for advanced EEG emotion recognition | [
"Jinfeng Wang",
"Yanhao Huang",
"Sifan Song",
"Boqian Wang",
"Jionglong Su",
"Jiaman Ding"
] | https://papers.miccai.org/miccai-2025/0024-Paper0161.html | https://papers.miccai.org/miccai-2025/paper/0161_paper.pdf | 10.1007/978-3-032-05162-2_2 | https://rdcu.be/eHc3A | null | [
"Body -> Brain",
"Modalities -> EEG / ECG",
"Applications -> Other",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/YanhaoHuang23/FAT"
] | [
"https://bcmi.sjtu.edu.cn/~seed/",
"https://bcmi.sjtu.edu.cn/~seed/seed-iv.html",
"https://bcmi.sjtu.edu.cn/~seed/seed-v.html",
"https://bcmi.sjtu.edu.cn/~seed/seed-vii.html",
"https://www.eecs.qmul.ac.uk/mmv/datasets/deap/"
] | 13-23 | @InProceedings{WanJin_Anovel_MICCAI2025,
author = { Wang, Jinfeng AND Huang, Yanhao AND Song, Sifan AND Wang, Boqian AND Su, Jionglong AND Ding, Jiaman},
title = { { A novel Fourier Adjacency Transformer for advanced EEG emotion recognition } },
booktitle = {proceedings of Medical Image Computin... | EEG emotion recognition faces significant hurdles due to noise interference, signal nonstationarity, and the inherent complexity of brain activity which make accurately emotion classification. In this study, we present the Fourier Adjacency Transformer, a novel framework that seamlessly integrates Fourier-based periodi... | 2503.13465 | title_snapshot |
Paper4548 | A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation | [
"Shuwei Xing",
"Derek W. Cool",
"David Tessier",
"Elvis C. S. Chen",
"Terry M. Peters",
"Aaron Fenster"
] | https://papers.miccai.org/miccai-2025/0025-Paper4548.html | https://papers.miccai.org/miccai-2025/paper/4548_paper.pdf | 10.1007/978-3-032-05114-1_3 | https://rdcu.be/eHw0T | null | [
"Body -> Abdomen",
"Modalities -> Ultrasound",
"Applications -> Image Registration",
"Applications -> Image-Guided Interventions",
"Machine Learning -> Deep Learning",
"Surgery -> Navigation",
"Special Topic -> MIC and CAI Solutions for Limited-Resource Environments"
] | [
"https://github.com/Xingorno/MICCAI2025-2DUS-to-CTMRI-Multimodal-Registration"
] | [] | 24-34 | @InProceedings{XinShu_ANovel_MICCAI2025,
author = { Xing, Shuwei AND Cool, Derek W. AND Tessier, David AND Chen, Elvis C. S. AND Peters, Terry M. AND Fenster, Aaron},
title = { { A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation } },
booktitle = {proceedings... | 3D ultrasound (US) imaging has shown significant benefits in enhancing the outcomes of percutaneous liver tumour ablation. Its clinical integration is crucial for transitioning 3D US into the therapeutic domain. However, challenges of tumour identification in US images continue to hinder its broader adoption. In this w... | 2506.21162 | title_snapshot |
Paper3289 | A Novel Streamline-based diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection | [
"Junyi Wang",
"Mubai Du",
"Ye Wu",
"Yijie Li",
"William M. Wells III",
"Lauren J. O’Donnell",
"Fan Zhang"
] | https://papers.miccai.org/miccai-2025/0026-Paper3289.html | https://papers.miccai.org/miccai-2025/paper/3289_paper.pdf | 10.1007/978-3-032-05162-2_3 | https://rdcu.be/eHc3B | null | [
"Body -> Brain",
"Modalities -> MRI - Diffusion Imaging",
"Applications -> Image Registration",
"Machine Learning -> Deep Learning"
] | [] | [] | 24-34 | @InProceedings{WanJun_ANovel_MICCAI2025,
author = { Wang, Junyi AND Du, Mubai AND Wu, Ye AND Li, Yijie AND Wells III, William M. AND O’Donnell, Lauren J. AND Zhang, Fan},
title = { { A Novel Streamline-based diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection } },
... | Registration of diffusion MRI tractography is an essential step for analyzing group similarities and variations in the brain’s white matter (WM). Streamline-based registration approaches can leverage the 3D geometric information of fiber pathways to enable spatial alignment after registration. Existing methods usually ... | 2503.02481 | title_snapshot |
Paper5419 | A Prior-Driven Lightweight Network for Endoscopic Exposure Correction | [
"Zhijian Wu",
"Hong Wang",
"Yuxuan Shi",
"Dingjiang Huang",
"Yefeng Zheng"
] | https://papers.miccai.org/miccai-2025/0027-Paper5419.html | https://papers.miccai.org/miccai-2025/paper/5419_paper.pdf | 10.1007/978-3-032-05141-7_2 | https://rdcu.be/eHw5M | null | [
"Body -> Abdomen",
"Modalities -> Endoscopy",
"Applications -> Other"
] | [] | [] | 13-23 | @InProceedings{WuZhi_APriorDriven_MICCAI2025,
author = { Wu, Zhijian AND Wang, Hong AND Shi, Yuxuan AND Huang, Dingjiang AND Zheng, Yefeng},
title = { { A Prior-Driven Lightweight Network for Endoscopic Exposure Correction } },
booktitle = {proceedings of Medical Image Computing and Computer Ass... | Against this endoscopic exposure correction task, although some past studies have yielded promising results, these methods do not fully explore the task-specific priors, and they generally require a large number of parameters thus compromising their applications on resource-constrained devices. In this paper, we carefu... | null | null |
Paper0938 | A Semi-Supervised Knowledge Distillation Framework for Left Ventricle Segmentation and Landmark Detection in Echocardiograms | [
"Haoyuan Chen",
"Yonghao Li",
"Long Yang",
"Han Wu",
"Lin Zhou",
"Kaicong Sun",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2025/0028-Paper0938.html | https://papers.miccai.org/miccai-2025/paper/0938_paper.pdf | 10.1007/978-3-032-04984-1_4 | https://rdcu.be/eHwX8 | null | [
"Body -> Cardiac",
"Modalities -> Photograph / Video",
"Modalities -> Ultrasound",
"Applications -> Computer Aided Diagnosis",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/chenhy-97/TSTNet"
] | [
"https://www.creatis.insa-lyon.fr/Challenge/camus/"
] | 34-43 | @InProceedings{CheHao_ASemiSupervised_MICCAI2025,
author = { Chen, Haoyuan AND Li, Yonghao AND Yang, Long AND Wu, Han AND Zhou, Lin AND Sun, Kaicong AND Shen, Dinggang},
title = { { A Semi-Supervised Knowledge Distillation Framework for Left Ventricle Segmentation and Landmark Detection in Echocardiogra... | Left ventricular segmentation and landmark detection from echocardiograms are routine practices in clinical settings for comprehensive cardiovascular disease evaluation. Recently, deep learning-based models have been developed to interpret echocardiograms. However, existing methods face challenges in handling sparse an... | null | null |
Paper0536 | A Two-Stage Method for Specular Highlight Detection and Removal in Medical Images | [
"Zefeng Li",
"Mingyue Cui",
"Daosong Hu",
"Jin Gong",
"Jingchong Weng",
"Zeyu Zhang",
"Lele Tian",
"Mengran Li",
"Kai Huang"
] | https://papers.miccai.org/miccai-2025/0029-Paper0536.html | https://papers.miccai.org/miccai-2025/paper/0536_paper.pdf | 10.1007/978-3-032-05127-1_3 | https://rdcu.be/eHw3a | null | [
"Body -> Eye",
"Modalities -> Endoscopy",
"Modalities -> Photograph / Video",
"Applications -> Image Segmentation",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/tkllndxn/highlight-removal"
] | [
"https://pan.baidu.com/s/1d8TOgcwZGD7f9aOqfudIOw?pwd=hyc4"
] | 23-33 | @InProceedings{LiZef_ATwoStage_MICCAI2025,
author = { Li, Zefeng AND Cui, Mingyue AND Hu, Daosong AND Gong, Jin AND Weng, Jingchong AND Zhang, Zeyu AND Tian, Lele AND Li, Mengran AND Huang, Kai},
title = { { A Two-Stage Method for Specular Highlight Detection and Removal in Medical Images } },
b... | In minimally invasive surgeries, such as endoscopic and ophthalmic procedures, specular highlights on tissue and instrument surfaces can obscure critical details, compromising surgical safety and precision. Traditional methods rely on color segmentation and filtering optimization but are highly sensitive to lighting va... | null | null |
Paper3485 | A Unified Continuous Staging Framework for Alzheimer’s Disease and Lewy Body Dementia via Hierarchical Anatomical Features | [
"Tong Chen",
"Minheng Chen",
"Jing Zhang",
"Yan Zhuang",
"Chao Cao",
"Xiaowei Yu",
"Yanjun Lyu",
"Lu Zhang",
"Li Su",
"Tianming Liu",
"Dajiang Zhu"
] | https://papers.miccai.org/miccai-2025/0030-Paper3485.html | https://papers.miccai.org/miccai-2025/paper/3485_paper.pdf | 10.1007/978-3-032-04947-6_2 | https://rdcu.be/eHwPo | null | [
"Body -> Brain",
"Modalities -> MRI",
"Modalities -> MRI - Diffusion Imaging",
"Applications -> Brain Network Analysis",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Interpretability / Explainability"
] | [
"https://github.com/tongchen2010/haff"
] | [] | 13-23 | @InProceedings{CheTon_AUnified_MICCAI2025,
author = { Chen, Tong AND Chen, Minheng AND Zhang, Jing AND Zhuang, Yan AND Cao, Chao AND Yu, Xiaowei AND Lyu, Yanjun AND Zhang, Lu AND Su, Li AND Liu, Tianming AND Zhu, Dajiang},
title = { { A Unified Continuous Staging Framework for Alzheimer’s Disease and Le... | Alzheimer’s Disease (AD) and Lewy Body Dementia (LBD) often exhibit overlapping pathologies, leading to common symptoms that make diagnosis challenging and protracted in clinical settings. While many studies achieve promising accuracy in identifying AD and LBD at earlier stages, they often focus on discrete classificat... | null | null |
Paper1980 | A Unified Missing Modality Imputation Model with Inter-Modality Contrastive and Consistent Learning | [
"Liangce Qi",
"Yusi Liu",
"Yuqin Li",
"Weili Shi",
"Guanyuan Feng",
"Zhengang Jiang"
] | https://papers.miccai.org/miccai-2025/0031-Paper1980.html | https://papers.miccai.org/miccai-2025/paper/1980_paper.pdf | 10.1007/978-3-032-04984-1_5 | https://rdcu.be/eHwX9 | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [] | [] | 44-53 | @InProceedings{QiLia_AUnified_MICCAI2025,
author = { Qi, Liangce AND Liu, Yusi AND Li, Yuqin AND Shi, Weili AND Feng, Guanyuan AND Jiang, Zhengang},
title = { { A Unified Missing Modality Imputation Model with Inter-Modality Contrastive and Consistent Learning } },
booktitle = {proceedings of Me... | Multi-modality magnetic resonance imaging (MRI) is widely used in the clinical diagnosis of brain tumors. However, the issue of missing modalities is frequently encountered in the real-world setting and can lead to the collapse of deep-learning-based automatic diagnosis algorithms that rely on full-modality images. To ... | null | null |
Paper2308 | A Uniform Multi-mode Fused Framework for Velocity Field Estimation in Ultrasound Imaging | [
"Hailong Li",
"Liansheng Wang",
"Yinran Chen"
] | https://papers.miccai.org/miccai-2025/0032-Paper2308.html | https://papers.miccai.org/miccai-2025/paper/2308_paper.pdf | 10.1007/978-3-032-04984-1_6 | https://rdcu.be/eHwYa | null | [
"Body -> Cardiac",
"Modalities -> Ultrasound",
"Applications -> Other"
] | [] | [] | 54-63 | @InProceedings{LiHai_AUniform_MICCAI2025,
author = { Li, Hailong AND Wang, Liansheng AND Chen, Yinran},
title = { { A Uniform Multi-mode Fused Framework for Velocity Field Estimation in Ultrasound Imaging } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention... | Velocity field estimation, or motion tracking, is the key to characterizing tissue function in ultrasound imaging. Current velocity field estimation remains challenging in cross-range motion tracking due to the less sensitivity of ultrasound in this dimension. In addition, there is a lack of a uniform framework for dif... | null | null |
Paper1142 | A Virtual Domain Collaborative Learning Framework for Semi-supervised Microscopic Hyperspectral Image Segmentation | [
"Geng Qin",
"Huan Liu",
"Wei Li",
"Haihao Zhang",
"Yuxing Guo"
] | https://papers.miccai.org/miccai-2025/0033-Paper1142.html | https://papers.miccai.org/miccai-2025/paper/1142_paper.pdf | 10.1007/978-3-032-05325-1_3 | https://rdcu.be/eHxdX | null | [
"Body -> Abdomen",
"Body -> other",
"Modalities -> Microscopy",
"Modalities -> Spectroscopy",
"Applications -> Computational (Integrative) Pathology",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/Qugeryolo/Virual-Domain"
] | [
"https://www.kaggle.com/datasets/ethelzq/multidimensional-choledoch-database/"
] | 24-34 | @InProceedings{QinGen_AVirtual_MICCAI2025,
author = { Qin, Geng AND Liu, Huan AND Li, Wei AND Zhang, Haihao AND Guo, Yuxing},
title = { { A Virtual Domain Collaborative Learning Framework for Semi-supervised Microscopic Hyperspectral Image Segmentation } },
booktitle = {proceedings of Medical Im... | Microscopic hyperspectral image segmentation faces dual challenges of limited labeled data and insufficient utilization of unlabeled data. However, existing semi-supervised methods often isolate the training processes for labeled and unlabeled data, neglecting their potential synergistic effects. To address this, we pr... | null | null |
Paper4381 | Abnormality-Driven Representation Learning for Radiology Imaging | [
"Marta Ligero",
"Tim Lenz",
"Georg Wölflein",
"Omar S. M. El Nahhas",
"Daniel Truhn",
"Jakob Nikolas Kather"
] | https://papers.miccai.org/miccai-2025/0034-Paper4381.html | https://papers.miccai.org/miccai-2025/paper/4381_paper.pdf | 10.1007/978-3-032-04965-0_2 | https://rdcu.be/eHaVi | null | [
"Body -> Abdomen",
"Body -> Breast",
"Modalities -> CT / X-Ray",
"Applications -> Anomaly Detection",
"Applications -> Integration of Imaging with Non-Imaging Biomarkers",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models",
"Machine Learning -> Semi- / Weakly- / Self-supervis... | [
"https://github.com/KatherLab/CLEAR"
] | [] | 14-24 | @InProceedings{LigMar_AbnormalityDriven_MICCAI2025,
author = { Ligero, Marta AND Lenz, Tim AND Wölflein, Georg AND El Nahhas, Omar S. M. AND Truhn, Daniel AND Kather, Jakob Nikolas},
title = { { Abnormality-Driven Representation Learning for Radiology Imaging } },
booktitle = {proceedings of Med... | Radiology deep learning pipelines predominantly employ end-to-end 3D networks based on models pre-trained on other tasks, which are then fine-tuned on the task at hand. In contrast, adjacent medical fields such as pathology, which focus on 2D images, have effectively adopted task-agnostic foundational models based on s... | 2411.16803 | title_snapshot |
Paper3082 | Accelerated Free-Breathing 5D Multi-Echo Respiratory Motion-Resolved R2*, PDFF, and QSM Using Novel Composite Total Variation | [
"MungSoo Kang",
"Or Alus",
"Youngwook Kee"
] | https://papers.miccai.org/miccai-2025/0035-Paper3082.html | https://papers.miccai.org/miccai-2025/paper/3082_paper.pdf | 10.1007/978-3-032-04947-6_3 | https://rdcu.be/eHwPp | null | [
"Body -> Abdomen",
"Modalities -> MRI",
"Applications -> Other"
] | [] | [] | 24-34 | @InProceedings{KanMun_Accelerated_MICCAI2025,
author = { Kang, MungSoo AND Alus, Or AND Kee, Youngwook},
title = { { Accelerated Free-Breathing 5D Multi-Echo Respiratory Motion-Resolved R2*, PDFF, and QSM Using Novel Composite Total Variation } },
booktitle = {proceedings of Medical Image Comput... | We introduce a novel composite total variation (TV) and its solution algorithm with their application to multi-echo, respiratory motion-resolved 5D (3D space + 1D respiratory motion + 1D echo signal evolution) compressed sensing (CS) abdominal MR image reconstruction. The proposed formalism ensures a sparse representat... | null | null |
Paper1958 | Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound | [
"Jian Wang",
"Qiongying Ni",
"Hongkui Yu",
"Ruixuan Yao",
"Jinqiao Ying",
"Bin Zhang",
"Xingyi Yang",
"Jin Peng",
"Jiongquan Chen",
"Junxuan Yu",
"Wenlong Shi",
"Chaoyu Chen",
"Zhongnuo Yan",
"Mingyuan Luo",
"Gaocheng Cai",
"Dong Ni",
"Jing Lu",
"Xin Yang"
] | https://papers.miccai.org/miccai-2025/0036-Paper1958.html | https://papers.miccai.org/miccai-2025/paper/1958_paper.pdf | 10.1007/978-3-032-04927-8_4 | https://rdcu.be/eHwKO | null | [
"Body -> Fetal / Pediatric Imaging",
"Modalities -> Ultrasound",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/Qioy-i/EFW"
] | [] | 34-44 | @InProceedings{WanJia_Accurate_MICCAI2025,
author = { Wang, Jian AND Ni, Qiongying AND Yu, Hongkui AND Yao, Ruixuan AND Ying, Jinqiao AND Zhang, Bin AND Yang, Xingyi AND Peng, Jin AND Chen, Jiongquan AND Yu, Junxuan AND Shi, Wenlong AND Chen, Chaoyu AND Yan, Zhongnuo AND Luo, Mingyuan AND Cai, Gaocheng AND Ni, ... | Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D stand... | 2507.00398 | title_snapshot |
Paper2379 | Accurate Boundary Alignment and Realism Enhancement for Colonoscopic Polyp Image-Mask Pair Generation | [
"Riyu Qiu",
"Kun Xia",
"Feng Gao",
"Shuting Yang",
"Du Cai",
"Jiacheng Wang",
"Yinran Chen",
"Liansheng Wang"
] | https://papers.miccai.org/miccai-2025/0037-Paper2379.html | https://papers.miccai.org/miccai-2025/paper/2379_paper.pdf | 10.1007/978-3-032-05127-1_4 | https://rdcu.be/eHw3b | null | [
"Body -> other",
"Modalities -> Endoscopy",
"Applications -> Image Segmentation",
"Applications -> Image Synthesis / Augmentation / Super-Resolution"
] | [
"https://github.com/16rq/Polyp-LDM"
] | [] | 34-44 | @InProceedings{QiuRiy_Accurate_MICCAI2025,
author = { Qiu, Riyu AND Xia, Kun AND Gao, Feng AND Yang, Shuting AND Cai, Du AND Wang, Jiacheng AND Chen, Yinran AND Wang, Liansheng},
title = { { Accurate Boundary Alignment and Realism Enhancement for Colonoscopic Polyp Image-Mask Pair Generation } },
... | Polyp segmentation is the foundation of colonoscopic lesion screening, diagnosis, and therapy. However, the data size of images and annotations is limited. The latent diffusion model (LDM) has emerged as a powerful tool in synthesizing high-quality medical images with low computational costs. However, the challenges of... | null | null |
Paper3160 | Active Source-Free Cross-Domain and Cross-Modality Adaptation for Volumetric Medical Image Segmentation by Image Sensitivity and Organ Heterogeneity Sampling | [
"Jin Yang",
"Xiaobing Yu",
"Peijie Qiu",
"Daniel Marcus",
"Aristeidis Sotiras"
] | https://papers.miccai.org/miccai-2025/0038-Paper3160.html | https://papers.miccai.org/miccai-2025/paper/3160_paper.pdf | 10.1007/978-3-032-04978-0_1 | https://rdcu.be/eHdSj | null | [
"Body -> Abdomen",
"Modalities -> CT / X-Ray",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization"
] | [] | [] | 3-12 | @InProceedings{YanJin_Active_MICCAI2025,
author = { Yang, Jin AND Yu, Xiaobing AND Qiu, Peijie AND Marcus, Daniel AND Sotiras, Aristeidis},
title = { { Active Source-Free Cross-Domain and Cross-Modality Adaptation for Volumetric Medical Image Segmentation by Image Sensitivity and Organ Heterogeneity Sam... | Deep learning (DL) methods have achieved great success in medical image segmentation, but they are challenged to demonstrate robust performance across different datasets due to domain and modality gaps. The Source-Free Domain Adaptation techniques adapt DL models to generalize across domains without access to source da... | null | null |
Paper0315 | ADA: An Adaptive Augmentation Framework for Single-Source Domain Generalization in Medical Image Segmentation | [
"Runlin Huang",
"Hongmin Cai",
"Weipeng Zhuo",
"Shangyan Cai",
"Haowei Lin",
"Wentao Fan",
"Weifeng Su"
] | https://papers.miccai.org/miccai-2025/0039-Paper0315.html | https://papers.miccai.org/miccai-2025/paper/0315_paper.pdf | 10.1007/978-3-032-05127-1_5 | https://rdcu.be/eHw3c | null | [
"Body -> other",
"Modalities -> CT / X-Ray",
"Modalities -> Endoscopy",
"Modalities -> Other",
"Machine Learning -> Deep Learning",
"Surgery -> Data Science",
"Special Topic -> Low-Cost and Point-of-Care Imaging Solutions"
] | [] | [] | 45-54 | @InProceedings{HuaRun_ADA_MICCAI2025,
author = { Huang, Runlin AND Cai, Hongmin AND Zhuo, Weipeng AND Cai, Shangyan AND Lin, Haowei AND Fan, Wentao AND Su, Weifeng},
title = { { ADA: An Adaptive Augmentation Framework for Single-Source Domain Generalization in Medical Image Segmentation } },
boo... | In medical image analysis, significant challenges arise from domain shifts. Models trained on one dataset often struggle to generalize to unseen domains, limiting their clinical utility. To overcome this challenge, recent advancements have tried to increase the diversity of training data with data augmentation, in whic... | null | null |
Paper1927 | Ada-FCN: Adaptive Frequency-Coupled Network for fMRI-Based Brain Disorder Classification | [
"Yue Xun",
"Jiaxing Xu",
"Wenbo Gao",
"Chen Yang",
"Shujun Wang"
] | https://papers.miccai.org/miccai-2025/0040-Paper1927.html | https://papers.miccai.org/miccai-2025/paper/1927_paper.pdf | 10.1007/978-3-032-05162-2_4 | https://rdcu.be/eHc4b | null | [
"Body -> Brain",
"Modalities -> MRI - Functional MRI",
"Applications -> Brain Network Analysis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/XXYY20221234/Ada-FCN"
] | [
"https://adni.loni.usc.edu/",
"https://fcon_1000.projects.nitrc.org/indi/abide/"
] | 35-45 | @InProceedings{XunYue_AdaFCN_MICCAI2025,
author = { Xun, Yue AND Xu, Jiaxing AND Gao, Wenbo AND Yang, Chen AND Wang, Shujun},
title = { { Ada-FCN: Adaptive Frequency-Coupled Network for fMRI-Based Brain Disorder Classification } },
booktitle = {proceedings of Medical Image Computing and Computer... | Resting-state fMRI has become a valuable tool for classifying brain disorders and constructing brain functional connectivity networks by tracking BOLD signals across brain regions. However, existing models largely neglect the multi-frequency nature of neuronal oscillations, treating BOLD signals as monolithic time seri... | 2511.04718 | title_snapshot |
Paper4230 | Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision | [
"Soham Walimbe",
"Britty Baby",
"Vinkle Srivastav",
"Nicolas Padoy"
] | https://papers.miccai.org/miccai-2025/0041-Paper4230.html | https://papers.miccai.org/miccai-2025/paper/4230_paper.pdf | 10.1007/978-3-032-05141-7_3 | https://rdcu.be/eHw5N | null | [
"Body -> Abdomen",
"Modalities -> Endoscopy",
"Modalities -> Photograph / Video",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models",
"Surgery -> Data Science"
] | [
"https://github.com/CAMMA-public/MML-SurgAdapt"
] | [
"https://github.com/CAMMA-public/TF-Cholec80",
"https://github.com/CAMMA-public/Endoscapes",
"https://github.com/CAMMA-public/cholect50"
] | 24-33 | @InProceedings{WalSoh_Adaptation_MICCAI2025,
author = { Walimbe, Soham AND Baby, Britty AND Srivastav, Vinkle AND Padoy, Nicolas},
title = { { Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision } },
booktitle = {proceedings of Medical Image Computing and Comp... | Surgical AI often involves multiple tasks within a single procedure, like phase recognition or assessing the Critical View of Safety in laparoscopic cholecystectomy. Traditional models, built for one task at a time, lack flexibility, requiring a separate model for each. To address this, we introduce MML-SurgAdapt, a un... | 2507.05020 | title_snapshot |
Paper0966 | ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis | [
"Yaofei Duan",
"Yuhao Huang",
"Xin Yang",
"Luyi Han",
"Xinyu Xie",
"Zhiyuan Zhu",
"Ping He",
"Ka-Hou Chan",
"Ligang Cui",
"Sio-Kei Im",
"Dong Ni",
"Tao Tan"
] | https://papers.miccai.org/miccai-2025/0042-Paper0966.html | https://papers.miccai.org/miccai-2025/paper/0966_paper.pdf | 10.1007/978-3-032-05325-1_4 | https://rdcu.be/eHxdY | null | [
"Body -> Breast",
"Modalities -> Ultrasound",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization",
"Machine Learning -> Uncertainty"
] | [
"https://github.com/miccai25-966/ADAptation"
] | [
"https://www.kaggle.com/datasets/sabahesaraki/breast-ultrasound-images-dataset",
"https://zenodo.org/records/8231412",
"https://www.nature.com/articles/s41597-025-04562-3"
] | 35-45 | @InProceedings{DuaYao_ADAptation_MICCAI2025,
author = { Duan, Yaofei AND Huang, Yuhao AND Yang, Xin AND Han, Luyi AND Xie, Xinyu AND Zhu, Zhiyuan AND He, Ping AND Chan, Ka-Hou AND Cui, Ligang AND Im, Sio-Kei AND Ni, Dong AND Tan, Tao},
title = { { ADAptation: Reconstruction-based Unsupervised Active Lea... | Deep learning-based diagnostic models often suffer performance drops due to distribution shifts between training (source) and test (target) domains. Collecting and labeling sufficient target domain data for model retraining represents an optimal solution, yet is limited by time and scarce resources. Active learning (AL... | 2507.00474 | title_snapshot |
Paper0146 | Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training | [
"Tao Luo",
"Han Wu",
"Tong Yang",
"Dinggang Shen",
"Zhiming Cui"
] | https://papers.miccai.org/miccai-2025/0043-Paper0146.html | https://papers.miccai.org/miccai-2025/paper/0146_paper.pdf | 10.1007/978-3-032-05325-1_5 | https://rdcu.be/eHxd0 | null | [
"Body -> other",
"Modalities -> CT / X-Ray",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/ShanghaiTech-IMPACT/DVCTNet"
] | [
"https://github.com/ShanghaiTech-IMPACT/DVCTNet"
] | 46-55 | @InProceedings{LuoTao_Adapting_MICCAI2025,
author = { Luo, Tao AND Wu, Han AND Yang, Tong AND Shen, Dinggang AND Cui, Zhiming},
title = { { Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training } },
booktitle = {proceedings of Medical Image Computing and Computer Assis... | Accurate dental caries detection from panoramic X-rays plays a pivotal role in preventing lesion progression. However, current detection methods often yield suboptimal accuracy due to subtle contrast variations and diverse lesion morphology of dental caries. In this work, inspired by the clinical workflow where dentist... | 2508.20813 | title_snapshot |
Paper1458 | Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation | [
"Xiaoran Zhang",
"Eric Z. Chen",
"Lin Zhao",
"Xiao Chen",
"Yikang Liu",
"Boris Maihe",
"James S. Duncan",
"Terrence Chen",
"Shanhui Sun"
] | https://papers.miccai.org/miccai-2025/0044-Paper1458.html | https://papers.miccai.org/miccai-2025/paper/1458_paper.pdf | 10.1007/978-3-032-04971-1_3 | https://rdcu.be/eHwS9 | null | [
"Body -> Cardiac",
"Modalities -> Ultrasound",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models"
] | [] | [
"https://www.creatis.insa-lyon.fr/Challenge/camus/",
"https://www.kaggle.com/datasets/xiaoweixumedicalai/cardiacudc-dataset",
"https://drive.google.com/file/d/1reHyY5eTZ5uePXMVMzFOq5j3eFOSp50F/view",
"https://aimi.stanford.edu/datasets/thyroid-ultrasound-cine-clip"
] | 24-34 | @InProceedings{ZhaXia_Adapting_MICCAI2025,
author = { Zhang, Xiaoran AND Chen, Eric Z. AND Zhao, Lin AND Chen, Xiao AND Liu, Yikang AND Maihe, Boris AND Duncan, James S. AND Chen, Terrence AND Sun, Shanhui},
title = { { Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation } },
... | We propose a novel approach that adapts hierarchical vision foundation models for real-time ultrasound image segmentation. Existing ultrasound segmentation methods often struggle with adaptability to new tasks, relying on costly manual annotations, while real-time approaches generally fail to match state-of-the-art per... | 2503.24368 | title_snapshot |
Paper2850 | Adaptive Adversarial Data Augmentation with Trajectory Constraint for Alzheimer’s Disease Conversion Prediction | [
"Hyuna Cho",
"Hayoung Ahn",
"Guorong Wu",
"Won Hwa Kim"
] | https://papers.miccai.org/miccai-2025/0045-Paper2850.html | https://papers.miccai.org/miccai-2025/paper/2850_paper.pdf | 10.1007/978-3-032-04981-0_2 | https://rdcu.be/eHwVX | null | [
"Body -> Brain",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Interpretability / Explainability"
] | [] | [] | 13-23 | @InProceedings{ChoHyu_Adaptive_MICCAI2025,
author = { Cho, Hyuna AND Ahn, Hayoung AND Wu, Guorong AND Kim, Won Hwa},
title = { { Adaptive Adversarial Data Augmentation with Trajectory Constraint for Alzheimer’s Disease Conversion Prediction } },
booktitle = {proceedings of Medical Image Computin... | Distinguishing progressive mild cognitive impairment (pMCI) from stable MCI (sMCI) is crucial for timely treatment of Alzheimer’s disease (AD), yet it is challenging due to inherent class imbalance and limited data. While recent data synthesis methods have shown successful results, they often disregard distributional d... | null | null |
Paper0949 | Adaptive Embedding for Long-Range High-Order Dependencies via Time-Varying Transformer on fMRI | [
"Rundong Xue",
"Xiangmin Han",
"Hao Hu",
"Zeyu Zhang",
"Shaoyi Du",
"Yue Gao"
] | https://papers.miccai.org/miccai-2025/0046-Paper0949.html | https://papers.miccai.org/miccai-2025/paper/0949_paper.pdf | 10.1007/978-3-032-05162-2_5 | https://rdcu.be/eHc4c | null | [
"Body -> Brain",
"Modalities -> MRI - Functional MRI",
"Applications -> Brain Network Analysis",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [] | [] | 46-55 | @InProceedings{XueRun_Adaptive_MICCAI2025,
author = { Xue, Rundong AND Han, Xiangmin AND Hu, Hao AND Zhang, Zeyu AND Du, Shaoyi AND Gao, Yue},
title = { { Adaptive Embedding for Long-Range High-Order Dependencies via Time-Varying Transformer on fMRI } },
booktitle = {proceedings of Medical Image... | Dynamic functional brain network analysis using rs-fMRI has emerged as a powerful approach to understanding brain disorders. However, current methods predominantly focus on pairwise brain region interactions, neglecting critical high-order dependencies and time-varying communication mechanisms. To address these limitat... | null | null |
Paper3136 | Adaptive Frame Selection for Gestational Age Estimation from Blind Sweep Fetal Ultrasound Videos | [
"Tanya Akumu",
"Marawan Elbatel",
"Victor M. Campello",
"Richard Osuala",
"Carlos Martin-Isla",
"Ignacio Valenzuela",
"Xiaomeng Li",
"Bishesh Khanal",
"Karim Lekadir"
] | https://papers.miccai.org/miccai-2025/0047-Paper3136.html | https://papers.miccai.org/miccai-2025/paper/3136_paper.pdf | 10.1007/978-3-032-05185-1_1 | https://rdcu.be/eHxbP | null | [
"Body -> Fetal / Pediatric Imaging",
"Modalities -> Ultrasound",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Validation"
] | [
"https://github.com/tanya-akumu/selectGA"
] | [] | 3-12 | @InProceedings{AkuTan_Adaptive_MICCAI2025,
author = { Akumu, Tanya AND Elbatel, Marawan AND Campello, Victor M. AND Osuala, Richard AND Martin-Isla, Carlos AND Valenzuela, Ignacio AND Li, Xiaomeng AND Khanal, Bishesh AND Lekadir, Karim},
title = { { Adaptive Frame Selection for Gestational Age Estimatio... | The blind sweep ultrasound protocol, coupled with artificial intelligence (AI), offers promising solutions for expanding ultrasound availability in low-resource settings. However, existing AI approaches for gestational age (GA) prediction using bind sweeps face challenges like reliance on manual segmentation, computati... | null | null |
Paper4507 | Adaptive Graph Learning with Multi-Graph Convolutions for Brain Disorder Classification | [
"Fuad Noman",
"Raphaël C.-W. Phan",
"Hernando Ombao",
"Chee-Ming Ting"
] | https://papers.miccai.org/miccai-2025/0048-Paper4507.html | https://papers.miccai.org/miccai-2025/paper/4507_paper.pdf | 10.1007/978-3-032-05162-2_6 | https://rdcu.be/eHc4d | null | [
"Body -> Brain",
"Modalities -> MRI - Functional MRI",
"Applications -> Brain Network Analysis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/MNFuad/AGMGC"
] | [
"http://preprocessed-connectomes-project.org/abide/",
"https://rfmri.org/REST-meta-MDD"
] | 56-65 | @InProceedings{NomFua_Adaptive_MICCAI2025,
author = { Noman, Fuad AND Phan, Raphaël C.-W. AND Ombao, Hernando AND Ting, Chee-Ming},
title = { { Adaptive Graph Learning with Multi-Graph Convolutions for Brain Disorder Classification } },
booktitle = {proceedings of Medical Image Computing and Com... | Functional Magnetic Resonance Imaging (fMRI) provides crucial insights into brain activity but presents challenges due to its high-dimensional, dynamic, and noisy nature. Traditional graph-based approaches for fMRI analysis often rely on predefined correlation structures, which may not accurately reflect the true under... | null | null |
Paper0326 | Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling | [
"Ningfeng Que",
"Xiaofei Wang",
"Jingjing Chen",
"Yixuan Jiang",
"Chao Li"
] | https://papers.miccai.org/miccai-2025/0049-Paper0326.html | https://papers.miccai.org/miccai-2025/paper/0326_paper.pdf | 10.1007/978-3-032-04927-8_5 | https://rdcu.be/eHwKP | null | [
"Body -> other",
"Modalities -> Microscopy",
"Modalities -> Other",
"Applications -> Computational (Integrative) Pathology",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/XiaofeiWang2018/C2-STi"
] | [
"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE251926"
] | 45-54 | @InProceedings{QueNin_Adaptive_MICCAI2025,
author = { Que, Ningfeng AND Wang, Xiaofei AND Chen, Jingjing AND Jiang, Yixuan AND Li, Chao},
title = { { Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling } },
booktitle = {proceedings of Medical Image Computing and C... | Spatial transcriptomics (ST) is a promising technique that characterizes the spatial gene profiling patterns within the tissue context. Comprehensive ST analysis depends on consecutive slices for 3D spatial insights, whereas the missing intermediate tissue sections and high costs limit the practical feasibility of gene... | 2505.10729 | title_snapshot |
Paper4658 | Adaptive Stain Normalization for Cross-Domain Medical Histology | [
"Tianyue Xu",
"Yanlin Wu",
"Abhai K. Tripathi",
"Matthew M. Ippolito",
"Benjamin D. Haeffele"
] | https://papers.miccai.org/miccai-2025/0050-Paper4658.html | https://papers.miccai.org/miccai-2025/paper/4658_paper.pdf | 10.1007/978-3-032-04981-0_3 | https://rdcu.be/eHwVY | null | [
"Body -> other",
"Modalities -> Microscopy",
"Applications -> Computational (Integrative) Pathology",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Domain Adaptation / Harmonization"
] | [
"https://github.com/xutianyue/BeerLaNet"
] | [
"https://zenodo.org/records/8358829",
"https://github.com/Shenggan/BCCD_Dataset",
"https://wilds.stanford.edu/datasets/#camelyon17"
] | 24-33 | @InProceedings{XuTia_Adaptive_MICCAI2025,
author = { Xu, Tianyue AND Wu, Yanlin AND Tripathi, Abhai K. AND Ippolito, Matthew M. AND Haeffele, Benjamin D.},
title = { { Adaptive Stain Normalization for Cross-Domain Medical Histology } },
booktitle = {proceedings of Medical Image Computing and Com... | Deep learning advances have revolutionized automated digital pathology analysis. However, variations in staining protocols and imaging conditions can introduce significant color variability. In deep learning, such color inconsistency often reduces performance when deploying models on data acquired under different condi... | 2510.06592 | title_snapshot |
Paper1831 | Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis | [
"Kunpeng Qiu",
"Zhiying Zhou",
"Yongxin Guo"
] | https://papers.miccai.org/miccai-2025/0051-Paper1831.html | https://papers.miccai.org/miccai-2025/paper/1831_paper.pdf | 10.1007/978-3-032-05127-1_6 | https://rdcu.be/eHw3d | null | [
"Body -> Abdomen",
"Modalities -> CT / X-Ray",
"Modalities -> Endoscopy",
"Applications -> Image Segmentation",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/Qiukunpeng/ADC"
] | [] | 55-65 | @InProceedings{QiuKun_Adaptively_MICCAI2025,
author = { Qiu, Kunpeng AND Zhou, Zhiying AND Guo, Yongxin},
title = { { Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis } },
booktitle = {proceedings of Medical Image Computing and Computer Assi... | Medical image annotation is constrained by privacy concerns and labor-intensive labeling, significantly limiting the performance and generalization of segmentation models. While mask-controllable diffusion models excel in synthesis, they struggle with precise lesion-mask alignment. We propose \textbf{Adaptively Distill... | 2507.23652 | title_snapshot |
Paper3573 | Addressing Label Scarcity and Domain Shift in Medical Image Segmentation | [
"Suruchi Kumari",
"Pravendra Singh"
] | https://papers.miccai.org/miccai-2025/0052-Paper3573.html | https://papers.miccai.org/miccai-2025/paper/3573_paper.pdf | 10.1007/978-3-032-04981-0_4 | https://rdcu.be/eHwVZ | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [] | [] | 34-44 | @InProceedings{KumSur_Addressing_MICCAI2025,
author = { Kumari, Suruchi AND Singh, Pravendra},
title = { { Addressing Label Scarcity and Domain Shift in Medical Image Segmentation } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
... | Limited labeled data and domain shifts present significant challenges for accurate medical image segmentation. Semi-supervised learning (SSL) and unsupervised domain adaptation (UDA) methods address these challenges individually. Existing SSL methods do not perform well in UDA scenarios, and vice versa. We observe that... | null | null |
Paper2088 | AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays | [
"Chenlang Yi",
"Zizhan Xiong",
"Qi Qi",
"Xiyuan Wei",
"Girish Bathla",
"Ching-Long Lin",
"Bobak J. Mortazavi",
"Tianbao Yang"
] | https://papers.miccai.org/miccai-2025/0053-Paper2088.html | https://papers.miccai.org/miccai-2025/paper/2088_paper.pdf | 10.1007/978-3-032-04978-0_2 | https://rdcu.be/eHdSk | null | [
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Algorithmic Fairness",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [] | [] | 13-23 | @InProceedings{YiChe_AdFairCLIP_MICCAI2025,
author = { Yi, Chenlang AND Xiong, Zizhan AND Qi, Qi AND Wei, Xiyuan AND Bathla, Girish AND Lin, Ching-Long AND Mortazavi, Bobak J. AND Yang, Tianbao},
title = { { AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays } },
... | Contrastive Language-Image Pre-training (CLIP) models have demonstrated superior performance across various visual tasks including medical image classification. However, fairness concerns, including demographic biases, have received limited attention for CLIP models. This oversight leads to critical issues, particularl... | 2506.23467 | title_snapshot |
Paper2328 | Advancing Medical Representation Learning Through High-Quality Data | [
"Negin Baghbanzadeh",
"Adibvafa Fallahpour",
"Yasaman Parhizkar",
"Franklin Ogidi",
"Shuvendu Roy",
"Sajad Ashkezari",
"Vahid Reza Khazaie",
"Michael Colacci",
"Ali Etemad",
"Arash Afkanpour",
"Elham Dolatabadi"
] | https://papers.miccai.org/miccai-2025/0054-Paper2328.html | https://papers.miccai.org/miccai-2025/paper/2328_paper.pdf | 10.1007/978-3-032-05169-1_3 | https://rdcu.be/eHw8d | null | [
"Body -> Breast",
"Body -> Lung",
"Body -> Skin",
"Modalities -> CT / X-Ray",
"Modalities -> Microscopy",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization",
"Machine Learning -> Foundation Models",
"Machine Lear... | [
"https://github.com/vectorInstitute/pmc-data-extraction/"
] | [
"https://huggingface.co/datasets/vector-institute/open-pmc",
"https://huggingface.co/vector-institute/open-pmc-clip"
] | 24-33 | @InProceedings{BagNeg_Advancing_MICCAI2025,
author = { Baghbanzadeh, Negin AND Fallahpour, Adibvafa AND Parhizkar, Yasaman AND Ogidi, Franklin AND Roy, Shuvendu AND Ashkezari, Sajad AND Khazaie, Vahid Reza AND Colacci, Michael AND Etemad, Ali AND Afkanpour, Arash AND Dolatabadi, Elham},
title = { { Adva... | Despite the growing scale of medical Vision-Language datasets, the impact of dataset quality on model performance remains under-explored. We introduce Open-PMC, a high-quality medical dataset from PubMed Central, containing 2.2 million image-text pairs, enriched with image modality annotations, subfigures, and summariz... | 2503.14377 | title_snapshot |
Paper0577 | AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation | [
"Lei Zhu",
"Jun Zhou",
"Rick Siow Mong Goh",
"Yong Liu"
] | https://papers.miccai.org/miccai-2025/0055-Paper0577.html | https://papers.miccai.org/miccai-2025/paper/0577_paper.pdf | 10.1007/978-3-032-05325-1_6 | https://rdcu.be/eHxd1 | null | [
"Body -> Abdomen",
"Body -> Cardiac",
"Body -> Skin",
"Modalities -> CT / X-Ray",
"Modalities -> MRI",
"Modalities -> Photograph / Video",
"Applications -> Image Segmentation",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/zlheui/AdvMIM"
] | [] | 56-66 | @InProceedings{ZhuLei_AdvMIM_MICCAI2025,
author = { Zhu, Lei AND Zhou, Jun AND Goh, Rick Siow Mong AND Liu, Yong},
title = { { AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted I... | Vision Transformer (ViT) has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised le... | 2506.20563 | title_snapshot |
Paper5183 | AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification | [
"Yunlong Zhang",
"Honglin Li",
"Yuxuan Sun",
"Zhongyi Shui",
"Jingxiong Li",
"Chenglu Zhu",
"Lin Yang"
] | https://papers.miccai.org/miccai-2025/0056-Paper5183.html | https://papers.miccai.org/miccai-2025/paper/5183_paper.pdf | 10.1007/978-3-032-04981-0_5 | https://rdcu.be/eHwV0 | null | [
"Body -> Breast",
"Modalities -> Microscopy",
"Applications -> Computational (Integrative) Pathology"
] | [
"https://github.com/dazhangyu123/AEM"
] | [] | 45-55 | @InProceedings{ZhaYun_AEM_MICCAI2025,
author = { Zhang, Yunlong AND Li, Honglin AND Sun, Yuxuan AND Shui, Zhongyi AND Li, Jingxiong AND Zhu, Chenglu AND Yang, Lin},
title = { { AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification } },
booktitl... | Multiple Instance Learning (MIL) effectively analyzes whole slide images but faces overfitting due to attention over-concentration. While existing solutions rely on complex architectural modifications or additional processing steps, we introduce Attention Entropy Maximization (AEM), a simple yet effective regularizatio... | 2406.15303 | title_snapshot |
Paper2411 | AffinityUMamba: Uncertainty-Aware Medical Image Segmentation via Probabilistic Weak Supervision Beyond Gold-Standard Annotations | [
"Yukun Zhang",
"Guisheng Wang",
"William Henry Nailon",
"Kun Cheng"
] | https://papers.miccai.org/miccai-2025/0057-Paper2411.html | https://papers.miccai.org/miccai-2025/paper/2411_paper.pdf | 10.1007/978-3-032-04947-6_4 | https://rdcu.be/eHwPq | null | [
"Body -> Abdomen",
"Body -> Brain",
"Body -> Cardiac",
"Modalities -> CT / X-Ray",
"Modalities -> MRI",
"Modalities -> Ultrasound",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning",
"Machine Learning -> Unce... | [] | [
"https://www.creatis.insa-lyon.fr/Challenge/acdc/",
"https://iseg2017.web.unc.edu/",
"https://www.med.upenn.edu/cbica/brats2020/",
"http://www.isles-challenge.org/",
"https://promise12.grand-challenge.org/",
"https://zmiclab.github.io/zxh/0/myops20/",
"http://medicaldecathlon.com/",
"https://amos22.gr... | 35-45 | @InProceedings{ZhaYuk_AffinityUMamba_MICCAI2025,
author = { Zhang, Yukun AND Wang, Guisheng AND Nailon, William Henry AND Cheng, Kun},
title = { { AffinityUMamba: Uncertainty-Aware Medical Image Segmentation via Probabilistic Weak Supervision Beyond Gold-Standard Annotations } },
booktitle = {pr... | Owing to its superior soft tissue contrast, Magnetic Resonance Imaging (MRI) has become a cornerstone modality in clinical practice. This prominence has driven extensive research on MRI-based segmentation, supported by the proliferation of publicly available benchmark datasets. Despite employing multi-expert consensus ... | null | null |
Paper1126 | All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior | [
"Haowei Chen",
"Zhiwen Yang",
"Haotian Hou",
"Hui Zhang",
"Bingzheng Wei",
"Gang Zhou",
"Yan Xu"
] | https://papers.miccai.org/miccai-2025/0058-Paper1126.html | https://papers.miccai.org/miccai-2025/paper/1126_paper.pdf | 10.1007/978-3-032-05325-1_7 | https://rdcu.be/eHxd2 | null | [
"Body -> Abdomen",
"Modalities -> Nuclear Imaging",
"Applications -> Image Synthesis / Augmentation / Super-Resolution"
] | [] | [] | 67-77 | @InProceedings{CheHao_AllinOne_MICCAI2025,
author = { Chen, Haowei AND Yang, Zhiwen AND Hou, Haotian AND Zhang, Hui AND Wei, Bingzheng AND Zhou, Gang AND Xu, Yan},
title = { { All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior } },
booktitle = {pr... | All-in-one medical image restoration (MedIR) aims to address multiple MedIR tasks using a unified model, concurrently recovering various high-quality (HQ) medical images (e.g., MRI, CT, and PET) from low-quality (LQ) counterparts. However, all-in-one MedIR presents significant challenges due to the heterogeneity across... | 2507.19874 | title_snapshot |
Paper0965 | Alzheimer’s Disease Recognition Based on Adaptive Graph Normalization Flow for Incomplete Multimodal Data Fusion | [
"Yaqin Li",
"Yihong Dong",
"Yanan Wu",
"Haihao Yan",
"Linlin Gao"
] | https://papers.miccai.org/miccai-2025/0059-Paper0965.html | https://papers.miccai.org/miccai-2025/paper/0965_paper.pdf | 10.1007/978-3-032-04984-1_7 | https://rdcu.be/eHwYb | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [] | [] | 64-73 | @InProceedings{LiYaq_Alzheimer’s_MICCAI2025,
author = { Li, Yaqin AND Dong, Yihong AND Wu, Yanan AND Yan, Haihao AND Gao, Linlin},
title = { { Alzheimer’s Disease Recognition Based on Adaptive Graph Normalization Flow for Incomplete Multimodal Data Fusion } },
booktitle = {proceedings of Medical... | Multimodal data holds significant value in the diagnosis of Alzheimer’s disease (AD). However, in real-world applications, factors such as privacy protection, acquisition costs, and sensor failures often lead to data missingness, posing challenges for incomplete multimodal learning. Currently the artificial intelligenc... | null | null |
Paper4859 | Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge | [
"Lalith Bharadwaj Baru",
"Kamalaker Dadi",
"Tapabrata Chakraborti",
"Raju S. Bapi"
] | https://papers.miccai.org/miccai-2025/0060-Paper4859.html | https://papers.miccai.org/miccai-2025/paper/4859_paper.pdf | 10.1007/978-3-032-04965-0_3 | https://rdcu.be/eHaVk | null | [
"Body -> Cardiac",
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Uncertainty"
] | [] | [] | 25-35 | @InProceedings{BarLal_Ambiguous_MICCAI2025,
author = { Baru, Lalith Bharadwaj AND Dadi, Kamalaker AND Chakraborti, Tapabrata AND Bapi, Raju S.},
title = { { Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge } },
booktitle = {proceedings of Medical Image Computing and Comput... | Accurate segmentation of medical images is challenging due to unclear lesion boundaries and mask variability. We introduce Segmentation Schödinger Bridge (SSB), the first application of Schödinger Bridge for ambiguous medical image segmentation, modelling joint image-mask dynamics to enhance performance. SSB preserves ... | 2509.17187 | title_snapshot |
Paper3121 | An Anatomical Significance-Aware Architecture for Explainable Myocardial Infarction Prediction via Multi-Task Learning | [
"Jiachuan Peng",
"Marcel Beetz",
"Abhirup Banerjee",
"Min Chen",
"Vicente Grau"
] | https://papers.miccai.org/miccai-2025/0061-Paper3121.html | https://papers.miccai.org/miccai-2025/paper/3121_paper.pdf | 10.1007/978-3-032-05185-1_2 | https://rdcu.be/eHxbQ | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Interpretability / Explainability"
] | [] | [
"https://www.ukbiobank.ac.uk/"
] | 13-23 | @InProceedings{PenJia_An_MICCAI2025,
author = { Peng, Jiachuan AND Beetz, Marcel AND Banerjee, Abhirup AND Chen, Min AND Grau, Vicente},
title = { { An Anatomical Significance-Aware Architecture for Explainable Myocardial Infarction Prediction via Multi-Task Learning } },
booktitle = {proceeding... | Myocardial infarction (MI) is a significant health burden globally. Its precise prediction is critical yet complicated by the functional complexities of the heart and heterogeneous clinical presentations. Although learning-based methods that model the 3D heart anatomy have been widely studied, improving cardiac embeddi... | null | null |
Paper4581 | Analysis of Image-and-Text Uncertainty Propagation in Multimodal Large Language Models with Cardiac MR-Based Applications | [
"Yucheng Tang",
"Yunguan Fu",
"Weixi Yi",
"Yipei Wang",
"Daniel C. Alexander",
"Rhodri Davies",
"Yipeng Hu"
] | https://papers.miccai.org/miccai-2025/0062-Paper4581.html | https://papers.miccai.org/miccai-2025/paper/4581_paper.pdf | 10.1007/978-3-032-04965-0_4 | https://rdcu.be/eHaVl | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Integration of Imaging with Non-Imaging Biomarkers",
"Machine Learning -> Foundation Models",
"Machine Learning -> Uncertainty"
] | [
"https://github.com/yucheng722/MUPM"
] | [] | 36-45 | @InProceedings{TanYuc_Analysis_MICCAI2025,
author = { Tang, Yucheng AND Fu, Yunguan AND Yi, Weixi AND Wang, Yipei AND Alexander, Daniel C. AND Davies, Rhodri AND Hu, Yipeng},
title = { { Analysis of Image-and-Text Uncertainty Propagation in Multimodal Large Language Models with Cardiac MR-Based Applicat... | Multimodal large language models (MLLMs) can process and integrate information from multimodality sources, such as text and images. However, interrelationship among input modalities, uncertainties due to individual uni-modal data and potential clinical applications following such an uncertainty decomposition are yet fu... | 2507.12945 | title_snapshot |
Paper3438 | Anatomical Graph-based Multilevel Distillation for Robust Alzheimer’s Disease Diagnosis with Missing Modalities | [
"Fei Liu",
"Huabin Wang",
"Mohamed Hisham Jaward",
"Shiuan-Ni Liang",
"Huey Fang Ong",
"Jiayuan Cheng"
] | https://papers.miccai.org/miccai-2025/0063-Paper3438.html | https://papers.miccai.org/miccai-2025/paper/3438_paper.pdf | 10.1007/978-3-032-04984-1_8 | https://rdcu.be/eHwYc | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/LiuFei-AHU/AGMD"
] | [
"https://adni.loni.usc.edu/"
] | 74-83 | @InProceedings{LiuFei_Anatomical_MICCAI2025,
author = { Liu, Fei AND Wang, Huabin AND Jaward, Mohamed Hisham AND Liang, Shiuan-Ni AND Ong, Huey Fang AND Cheng, Jiayuan},
title = { { Anatomical Graph-based Multilevel Distillation for Robust Alzheimer’s Disease Diagnosis with Missing Modalities } },
... | The multimodal model has shown superior potential for accurate Alzheimer’s disease (AD) diagnosis; however, its reliance on complete modalities limits its use in a clinical setting. This study proposes a novel Anatomical Graph-based Multilevel Distillation (AGMD) framework that effectively transfers multimodal knowledg... | null | null |
Paper0344 | Anatomical Structure Few-Shot Detection Utilizing Enhanced Human Anatomy Knowledge in Ultrasound Images | [
"Ying Zhu",
"Bocheng Liang",
"Ningshu Li",
"Lei Zhao",
"Xi Li",
"Hao Li",
"Fengwei Yang",
"Bin Pu"
] | https://papers.miccai.org/miccai-2025/0064-Paper0344.html | https://papers.miccai.org/miccai-2025/paper/0344_paper.pdf | 10.1007/978-3-032-04971-1_4 | https://rdcu.be/eHwTa | null | [
"Body -> Brain",
"Body -> Cardiac",
"Body -> Fetal / Pediatric Imaging",
"Modalities -> Ultrasound",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Surgery -> Other",
"Special Topic -> MIC and CAI Solutions for Limited-Resource Environments"
] | [
"https://github.com/yuyizhilian/TRR-CCM"
] | [] | 35-45 | @InProceedings{ZhuYin_Anatomical_MICCAI2025,
author = { Zhu, Ying AND Liang, Bocheng AND Li, Ningshu AND Zhao, Lei AND Li, Xi AND Li, Hao AND Yang, Fengwei AND Pu, Bin},
title = { { Anatomical Structure Few-Shot Detection Utilizing Enhanced Human Anatomy Knowledge in Ultrasound Images } },
bookt... | Deep learning-based models have significantly advanced clinical ultrasound tasks by detecting anatomical structures within vast ultrasound image datasets. However, their remarkable performance inherently requires extensive training of annotated medical datasets. Few-shot learning addresses the challenge of limited labe... | null | null |
Paper3005 | Anatomy-Aware Frequency-Attention Transformer Networks for Liver Couinaud CT/MR Segmentation | [
"Wenkang Fan",
"Hao Fang",
"Rui Li",
"Yanduan Lin",
"Chao An",
"Xiongbiao Luo"
] | https://papers.miccai.org/miccai-2025/0065-Paper3005.html | https://papers.miccai.org/miccai-2025/paper/3005_paper.pdf | 10.1007/978-3-032-04927-8_6 | https://rdcu.be/eHwKQ | null | [
"Body -> Abdomen",
"Modalities -> CT / X-Ray",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [] | [] | 55-65 | @InProceedings{FanWen_AnatomyAware_MICCAI2025,
author = { Fan, Wenkang AND Fang, Hao AND Li, Rui AND Lin, Yanduan AND An, Chao AND Luo, Xiongbiao},
title = { { Anatomy-Aware Frequency-Attention Transformer Networks for Liver Couinaud CT/MR Segmentation } },
booktitle = {proceedings of Medical Im... | Accurate Couinaud segmentation of liver CT/MR is essential in helping surgeons perceive the positional relationship between liver anatomy and intrahepatic lesions to make surgical planning. Unfortunately, current conventional and deep-learning based methods remain challenges in accurate Couinaud segmentation since the ... | null | null |
Paper2266 | Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning | [
"Runze Wang",
"Zeli Chen",
"Zhiyun Song",
"Wei Fang",
"Jiajin Zhang",
"Danyang Tu",
"Yuxing Tang",
"Minfeng Xu",
"Xianghua Ye",
"Le Lu",
"Dakai Jin"
] | https://papers.miccai.org/miccai-2025/0066-Paper2266.html | https://papers.miccai.org/miccai-2025/paper/2266_paper.pdf | 10.1007/978-3-032-04937-7_2 | https://rdcu.be/eHwMN | null | [
"Body -> Abdomen",
"Body -> Cardiac",
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Applications -> Image Segmentation",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [] | [] | 13-23 | @InProceedings{WanRun_AnatomyAware_MICCAI2025,
author = { Wang, Runze AND Chen, Zeli AND Song, Zhiyun AND Fang, Wei AND Zhang, Jiajin AND Tu, Danyang AND Tang, Yuxing AND Xu, Minfeng AND Ye, Xianghua AND Lu, Le AND Jin, Dakai},
title = { { Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models... | To reduce radiation exposure and improve the diagnostic efficacy of low-dose computed tomography (LDCT), numerous deep learning-based denoising methods have been developed to mitigate noise and artifacts. However, most of these approaches ignore the anatomical semantics of human tissues, which may potentially result in... | 2508.07788 | title_snapshot |
Paper0179 | Anatomy-based Self-supervised Pre-training for Scale-robust Hierarchical Representations in Chest X-rays | [
"Surong Chu",
"Yan Qiang",
"Guohua Ji",
"Xueting Ren",
"Lijing Zhang",
"Baoping Jia",
"Yangyang Wei",
"Juanjuan Zhao",
"Shuo Li"
] | https://papers.miccai.org/miccai-2025/0067-Paper0179.html | https://papers.miccai.org/miccai-2025/paper/0179_paper.pdf | 10.1007/978-3-032-05127-1_7 | https://rdcu.be/eHw3e | null | [
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning",
"Surgery -> Data Science",
"Special Topic -> MIC and CAI Solutions for Limited-Resource Environments"
] | [
"https://github.com/SurongChu/SRHRS"
] | [
"https://www.kaggle.com/datasets/nih-chest-xrays/data/data",
"https://lhncbc.nlm.nih.gov/LHC-downloads/dataset.html",
"https://www.kaggle.com/datasets/mustafaalgun/covid19-chest-xray-dataset",
"https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia",
"https://www.kaggle.com/c/siim-acr-pneumo... | 66-76 | @InProceedings{ChuSur_Anatomybased_MICCAI2025,
author = { Chu, Surong AND Qiang, Yan AND Ji, Guohua AND Ren, Xueting AND Zhang, Lijing AND Jia, Baoping AND Wei, Yangyang AND Zhao, Juanjuan AND Li, Shuo},
title = { { Anatomy-based Self-supervised Pre-training for Scale-robust Hierarchical Representations... | In self-supervised pre-training, learning consistent and hierarchical representations that capture relationships among anatomical semantics holds promise for enhancing the performance and interpretability of downstream tasks. However, the representations learned by existing methods are vulnerable to scale variations, w... | null | null |
Paper5303 | Anatomy-Conserving Unpaired CBCT-to-CT Translation via Schrödinger Bridge | [
"Ke Shi",
"Song Ouyang",
"Gang Liu",
"Yong Luo",
"Kehua Su",
"Zhiwen Liang",
"Bo Du"
] | https://papers.miccai.org/miccai-2025/0068-Paper5303.html | https://papers.miccai.org/miccai-2025/paper/5303_paper.pdf | 10.1007/978-3-032-04965-0_5 | https://rdcu.be/eHaVm | null | [
"Body -> Brain",
"Body -> other",
"Modalities -> CT / X-Ray",
"Modalities -> Other",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/Lalala-iks/ACSB"
] | [] | 46-55 | @InProceedings{ShiKe_AnatomyConserving_MICCAI2025,
author = { Shi, Ke AND Ouyang, Song AND Liu, Gang AND Luo, Yong AND Su, Kehua AND Liang, Zhiwen AND Du, Bo},
title = { { Anatomy-Conserving Unpaired CBCT-to-CT Translation via Schrödinger Bridge } },
booktitle = {proceedings of Medical Image Com... | Unpaired Cone-beam CT (CBCT)-to-CT translation is pivotal for radiotherapy planning, aiming to synergize CBCT’s clinical practicality with CT’s dosimetric precision. Existing methods, limited by scarce paired data and registration errors, struggle to preserve anatomical fidelity—a critical requirement to avoid incorrec... | null | null |
Paper2738 | Anatomy-Guided Multimodal Graph Networks for Alzheimer’s Disease: Integrative Analysis of Cross-Modal Brain Connectivity Signatures | [
"Wenzheng Hu",
"Zhenghua Guan",
"Peng Yang",
"Jiaqiang Li",
"Yi Liu",
"Shushen Gan",
"Tuo Cai",
"Ao Zhang",
"Tengda Zhang",
"Junlong Qu",
"Shaolong Wang",
"Gege Cai",
"Xiang Dong",
"Tianfu Wang",
"Baiying Lei"
] | https://papers.miccai.org/miccai-2025/0069-Paper2738.html | https://papers.miccai.org/miccai-2025/paper/2738_paper.pdf | 10.1007/978-3-032-05162-2_7 | https://rdcu.be/eHc4e | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Other"
] | [] | [] | 66-75 | @InProceedings{HuWen_AnatomyGuided_MICCAI2025,
author = { Hu, Wenzheng AND Guan, Zhenghua AND Yang, Peng AND Li, Jiaqiang AND Liu, Yi AND Gan, Shushen AND Cai, Tuo AND Zhang, Ao AND Zhang, Tengda AND Qu, Junlong AND Wang, Shaolong AND Cai, Gege AND Dong, Xiang AND Wang, Tianfu AND Lei, Baiying},
title =... | Multimodal neuroimaging grounded in standardized brain atlases enables precise decoding of Alzheimer’s progression by capturing both structural atrophy and functional decline across neural circuits. Current methods compromise anatomical fidelity in whole-brain modeling while generating biologically inconsistent cross-m... | null | null |
Paper2425 | Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture | [
"Nico Schulthess",
"Ender Konukoglu"
] | https://papers.miccai.org/miccai-2025/0070-Paper2425.html | https://papers.miccai.org/miccai-2025/paper/2425_paper.pdf | 10.1007/978-3-032-04947-6_5 | https://rdcu.be/eHwPr | null | [
"Body -> Brain",
"Body -> Eye",
"Body -> other",
"Modalities -> CT / X-Ray",
"Modalities -> MRI",
"Modalities -> Other",
"Applications -> Anomaly Detection",
"Machine Learning -> Foundation Models"
] | [
"https://github.com/NicoSchulthess/anomalydino-dpmm"
] | [
"https://github.com/DorisBao/BMAD"
] | 46-56 | @InProceedings{SchNic_Anomaly_MICCAI2025,
author = { Schulthess, Nico AND Konukoglu, Ender},
title = { { Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
... | In this work, we leverage informative embeddings from foundational models for unsupervised anomaly detection in medical imaging. For small datasets, a memory-bank of normative features can directly be used for anomaly detection which has been demonstrated recently. However, this is unsuitable for large medical datasets... | 2509.19997 | title_snapshot |
Paper3576 | Aorta Multi-class Segmentation via Anatomically Constrained Plane Detection | [
"Jonghoon An",
"Dong Hyun Lee",
"So Hyun Kim",
"Taejin Moon",
"Minyoung Chung"
] | https://papers.miccai.org/miccai-2025/0071-Paper3576.html | https://papers.miccai.org/miccai-2025/paper/3576_paper.pdf | 10.1007/978-3-032-04947-6_6 | https://rdcu.be/eHwPs | null | [
"Body -> Abdomen",
"Modalities -> CT / X-Ray",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/jjong0225/ACP"
] | [
"https://aortaseg24.grand-challenge.org/"
] | 57-66 | @InProceedings{AnJon_Aorta_MICCAI2025,
author = { An, Jonghoon AND Lee, Dong Hyun AND Kim, So Hyun AND Moon, Taejin AND Chung, Minyoung},
title = { { Aorta Multi-class Segmentation via Anatomically Constrained Plane Detection } },
booktitle = {proceedings of Medical Image Computing and Computer ... | Accurate multi-class segmentation of the aorta in medical CT images is essential for the effective diagnosis and treatment of blood flow abnormalities. However, achieving precise segmentation in multi-zone remains challenging due to the lack of visible boundaries and the similarity in intensity between zones. Although ... | null | null |
Paper3853 | Asymmetric Matching in Abdominal Lymph Nodes of Follow-up CT Scans | [
"Yiji Mao",
"Yi Zhang",
"Xinyu Zou",
"Yuling Zheng",
"Hao Huang",
"Haixian Zhang"
] | https://papers.miccai.org/miccai-2025/0072-Paper3853.html | https://papers.miccai.org/miccai-2025/paper/3853_paper.pdf | 10.1007/978-3-032-05114-1_4 | https://rdcu.be/eHw0U | null | [
"Body -> Abdomen",
"Modalities -> CT / X-Ray",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Surgery -> Scene Understanding",
"Surgery -> Skill and Work Flow Analysis",
"Special Topic -> Biomedical Image Computing for Neglected Diseases"
] | [
"https://github.com/maoyij/Asymmetric-Matching"
] | [] | 35-44 | @InProceedings{MaoYij_Asymmetric_MICCAI2025,
author = { Mao, Yiji AND Zhang, Yi AND Zou, Xinyu AND Zheng, Yuling AND Huang, Hao AND Zhang, Haixian},
title = { { Asymmetric Matching in Abdominal Lymph Nodes of Follow-up CT Scans } },
booktitle = {proceedings of Medical Image Computing and Compute... | Accurate tracking of abdominal lymph nodes (LN) across follow-up computed tomography (CT) scans is crucial for colorectal cancer staging and treatment response evaluation. However, establishing reliable LN correspondences remains underexplored due to challenges including scale variations, low resolution, difficulty dis... | null | null |
Paper1696 | Asynchronous Multi-Modal Learning for Dynamic Risk Monitoring of Acute Respiratory Distress Syndrome in Intensive Care Units | [
"Yidan Feng",
"Bohan Zhang",
"Sen Deng",
"Zhanli Hu",
"Jing Qin"
] | https://papers.miccai.org/miccai-2025/0073-Paper1696.html | https://papers.miccai.org/miccai-2025/paper/1696_paper.pdf | 10.1007/978-3-032-05182-0_2 | https://rdcu.be/eG4CS | null | [
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Applications -> Anomaly Detection",
"Applications -> Computer Aided Diagnosis",
"Applications -> Integration of Imaging with Non-Imaging Biomarkers"
] | [
"https://github.com/YidFeng/MICCAI25-ARDS-Risk-Prediction"
] | [] | 14-23 | @InProceedings{FenYid_Asynchronous_MICCAI2025,
author = { Feng, Yidan AND Zhang, Bohan AND Deng, Sen AND Hu, Zhanli AND Qin, Jing},
title = { { Asynchronous Multi-Modal Learning for Dynamic Risk Monitoring of Acute Respiratory Distress Syndrome in Intensive Care Units } },
booktitle = {proceedin... | Acute Respiratory Distress Syndrome (ARDS) is a critical adverse event with high modality rates, yet its recognition in ICU settings is often delayed. Clinicians face significant challenges in integrating asynchronous, multi-modal data streams with misaligned temporal resolutions during rapid deterioration. This work i... | null | null |
Paper3806 | Attention-Based Multimodal Deep Learning Model for Post-Stroke Motor Impairment Prediction | [
"Rukiye Karakis",
"Kali Gurkahraman",
"Georgios D. Mitsis",
"Marie-Hèléne Boudrias"
] | https://papers.miccai.org/miccai-2025/0074-Paper3806.html | https://papers.miccai.org/miccai-2025/paper/3806_paper.pdf | 10.1007/978-3-032-05182-0_3 | https://rdcu.be/eG4CV | null | [
"Body -> Brain",
"Modalities -> MRI",
"Modalities -> MRI - Diffusion Imaging",
"Applications -> Computer Aided Diagnosis",
"Applications -> Integration of Imaging with Non-Imaging Biomarkers",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/miccai3806/MotorImpairmentPrediction"
] | [] | 24-34 | @InProceedings{KarRuk_AttentionBased_MICCAI2025,
author = { Karakis, Rukiye AND Gurkahraman, Kali AND Mitsis, Georgios D. AND Boudrias, Marie-Hèléne},
title = { { Attention-Based Multimodal Deep Learning Model for Post-Stroke Motor Impairment Prediction } },
booktitle = {proceedings of Medical I... | Accurately predicting post-stroke motor impairment remains a challenge due to the complexity of functional recovery and its association with neuroimaging biomarkers. This study presents a deep learning (DL) framework that integrates Magnetic Resonance Imaging (MRI)-based measures such as Diffusion Tensor Imaging (DTI) ... | null | null |
Paper3774 | Attention-Guided Vector Quantized Variational Autoencoder for Brain Tumor Segmentation | [
"Danish Ali",
"Ajmal Mian",
"Naveed Akhtar",
"Ghulam Mubashar Hassan"
] | https://papers.miccai.org/miccai-2025/0075-Paper3774.html | https://papers.miccai.org/miccai-2025/paper/3774_paper.pdf | 10.1007/978-3-032-04927-8_7 | https://rdcu.be/eHwKR | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/danishali6421/AG-VQVAE-MICCAI"
] | [
"https://www.synapse.org/Synapse:syn25829067/wiki/610863"
] | 66-76 | @InProceedings{AliDan_AttentionGuided_MICCAI2025,
author = { Ali, Danish AND Mian, Ajmal AND Akhtar, Naveed AND Hassan, Ghulam Mubashar},
title = { { Attention-Guided Vector Quantized Variational Autoencoder for Brain Tumor Segmentation } },
booktitle = {proceedings of Medical Image Computing an... | Precise brain tumor segmentation is critical for effective treatment planning and radiotherapy. Existing methods rely on voxel-level supervision and often struggle to accurately delineate tumor boundaries, increasing potential surgical risks. We propose an Attention-Guided Vector Quantized Variational Autoencoder (AG-V... | null | null |
Paper5025 | Augmented Reality-based Guidance with Deformable Registration in Head and Neck Tumor Resection | [
"Qingyun Yang",
"Fangjie Li",
"Jiayi Xu",
"Zixuan Liu",
"Sindhura Sridhar",
"Whitney Jin",
"Jennifer Du",
"Jon Heiselman",
"Michael Miga",
"Michael Topf",
"Jie Ying Wu"
] | https://papers.miccai.org/miccai-2025/0076-Paper5025.html | https://papers.miccai.org/miccai-2025/paper/5025_paper.pdf | 10.1007/978-3-032-05114-1_5 | https://rdcu.be/eHw0V | null | [
"Body -> Skin",
"Modalities -> Photograph / Video",
"Applications -> Image Registration",
"Applications -> Image-Guided Interventions",
"Surgery -> Mixed / Augmented / Virtual Reality",
"Surgery -> Navigation",
"Surgery -> Scene Understanding"
] | [
"https://github.com/vu-maple-lab/Head-and-Neck-Tumor-Resection-Guidance"
] | [] | 45-54 | @InProceedings{YanQin_Augmented_MICCAI2025,
author = { Yang, Qingyun AND Li, Fangjie AND Xu, Jiayi AND Liu, Zixuan AND Sridhar, Sindhura AND Jin, Whitney AND Du, Jennifer AND Heiselman, Jon AND Miga, Michael AND Topf, Michael AND Wu, Jie Ying},
title = { { Augmented Reality-based Guidance with Deformabl... | Head and neck squamous cell carcinoma has one of the highest rates of recurrence. Recurrence rates can be reduced by accurate localization of positive margins. While frozen section analysis of resected specimens provides accurate intraoperative margin assessment, complex 3D anatomy and significant shrinkage of resected... | 2503.08802 | title_snapshot |
Paper3297 | Automated Auditing of Upper Endoscopy Procedure Times: A Temporal Multiclass Analysis | [
"Diego Bravo",
"Josué Ruano",
"Martín Gómez",
"Fabio A. Gónzalez",
"Eduardo Romero"
] | https://papers.miccai.org/miccai-2025/0077-Paper3297.html | https://papers.miccai.org/miccai-2025/paper/3297_paper.pdf | 10.1007/978-3-032-05141-7_4 | https://rdcu.be/eHw5O | null | [
"Body -> Abdomen",
"Modalities -> Endoscopy",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/Cimalab-unal/EndoAudit.git"
] | [
"https://doi.org/10.6084/m9.figshare.27308133"
] | 34-43 | @InProceedings{BraDie_Automated_MICCAI2025,
author = { Bravo, Diego AND Ruano, Josué AND Gómez, Martín AND Gónzalez, Fabio A. AND Romero, Eduardo},
title = { { Automated Auditing of Upper Endoscopy Procedure Times: A Temporal Multiclass Analysis } },
booktitle = {proceedings of Medical Image Com... | Upper endoscopy is the preferred method for detecting early-stagegastrointestinal diseases and plays a crucial role in managing gastric cancer. Quality assessment has been a recurring concern in clinical research, particularly regarding the time specialists spend examining different anatomical sites. While current guid... | null | null |
Paper2683 | Automated Characterization of Myocardial Scar Topological Patterns for Ventricular Tachycardia Screening | [
"Xicheng Sheng",
"Yang Zhang",
"Lei Li",
"Bailiang Chen",
"Freddy Odille",
"Xiahai Zhuang"
] | https://papers.miccai.org/miccai-2025/0078-Paper2683.html | https://papers.miccai.org/miccai-2025/paper/2683_paper.pdf | 10.1007/978-3-032-04947-6_7 | https://rdcu.be/eHwPt | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Computational Anatomy and Physiology"
] | [
"https://github.com/Sheng-xc/VTS_PolarNet"
] | [] | 67-76 | @InProceedings{SheXic_Automated_MICCAI2025,
author = { Sheng, Xicheng AND Zhang, Yang AND Li, Lei AND Chen, Bailiang AND Odille, Freddy AND Zhuang, Xiahai},
title = { { Automated Characterization of Myocardial Scar Topological Patterns for Ventricular Tachycardia Screening } },
booktitle = {proc... | Ventricular tachycardia screening is crucial for early intervention and prevention of life-threatening cardiac events. Myocardial scar topology on late gadolinium enhancement (LGE) MRI offers detailed structural insights that may be closely associated with the mechanisms underlying ventricular tachycardia. However, acc... | null | null |
Paper4285 | Automated Detection of Abnormalities in Zebrafish Development | [
"Sarath Sivaprasad",
"Hui-Po Wang",
"Anna-Lisa Jäckel",
"Jonas Baumann",
"Carole Baumann",
"Jennifer Herrmann",
"Mario Fritz"
] | https://papers.miccai.org/miccai-2025/0079-Paper4285.html | https://papers.miccai.org/miccai-2025/paper/4285_paper.pdf | 10.1007/978-3-032-04981-0_6 | https://rdcu.be/eHwV1 | null | [
"Body -> other",
"Modalities -> Microscopy",
"Applications -> Anomaly Detection"
] | [
"https://github.com/sarathsp1729/Zebrafish-development"
] | [
"https://github.com/sarathsp1729/Zebrafish-development"
] | 56-66 | @InProceedings{SivSar_Automated_MICCAI2025,
author = { Sivaprasad, Sarath AND Wang, Hui-Po AND Jäckel, Anna-Lisa AND Baumann, Jonas AND Baumann, Carole AND Herrmann, Jennifer AND Fritz, Mario},
title = { { Automated Detection of Abnormalities in Zebrafish Development } },
booktitle = {proceeding... | Zebrafish embryos are a valuable model for drug discovery due to their optical transparency and genetic similarity to humans. However, current evaluations rely on manual inspection, which is costly and labor-intensive. While machine learning offers automation potential, progress is limited by the lack of comprehensive ... | 2605.10464 | title_snapshot |
Paper5442 | Automated Detection of BK Virus in H&E Whole-Slide Images Using Weakly-Supervised Deep Learning and Interpretable Morphological Biomarkers | [
"Sharifa Sahai",
"Ana D. Ramos-Guerra",
"Cristina Almagro-Pérez",
"Guillaume Jaume",
"Andrew Zhang",
"Helmut Rennke",
"Astrid Weins",
"Juan E. Ortuño",
"Maria J. Ledesma-Carbayo",
"Faisal Mahmood"
] | https://papers.miccai.org/miccai-2025/0080-Paper5442.html | https://papers.miccai.org/miccai-2025/paper/5442_paper.pdf | 10.1007/978-3-032-04984-1_9 | https://rdcu.be/eHwYd | null | [
"Body -> Abdomen",
"Body -> other",
"Modalities -> Microscopy",
"Applications -> Computational (Integrative) Pathology",
"Machine Learning -> Interpretability / Explainability",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning",
"Machine Learning -> Validation"
] | [] | [] | 84-94 | @InProceedings{SahSha_Automated_MICCAI2025,
author = { Sahai, Sharifa AND Ramos-Guerra, Ana D. AND Almagro-Pérez, Cristina AND Jaume, Guillaume AND Zhang, Andrew AND Rennke, Helmut AND Weins, Astrid AND Ortuño, Juan E. AND Ledesma-Carbayo, Maria J. AND Mahmood, Faisal},
title = { { Automated Detection o... | Detecting BK Virus (BKV) is crucial for managing post-transplant outcomes in kidney patients. While BKV is typically identified using SV40 immunohistochemistry (IHC), this method is time-consuming, limited by tissue availability and resource-intensive, especially in low-resource settings. Recent advances in computation... | null | null |
Paper4185 | Automated Integration of Surgical Implants into Digital Twins for Trauma Surgery | [
"Tobias Stauffer",
"Manuel Reber",
"Léon Fellmann",
"Reto Babst",
"Mirko Meboldt",
"Quentin Lohmeyer"
] | https://papers.miccai.org/miccai-2025/0081-Paper4185.html | https://papers.miccai.org/miccai-2025/paper/4185_paper.pdf | 10.1007/978-3-032-05114-1_6 | https://rdcu.be/eHw0W | null | [
"Body -> other",
"Modalities -> Other",
"Applications -> Image-Guided Interventions",
"Surgery -> Navigation",
"Surgery -> Planning and Simulation",
"Surgery -> Scene Understanding",
"Surgery -> Skill and Work Flow Analysis"
] | [] | [] | 55-64 | @InProceedings{StaTob_Automated_MICCAI2025,
author = { Stauffer, Tobias AND Reber, Manuel AND Fellmann, Léon AND Babst, Reto AND Meboldt, Mirko AND Lohmeyer, Quentin},
title = { { Automated Integration of Surgical Implants into Digital Twins for Trauma Surgery } },
booktitle = {proceedings of Me... | A digital twin (DT) is a dynamic virtual model that mirrors a physical system, with promising applications in surgical planning, guidance, and outcome assessment. While DTs can represent various key aspects of surgery, such as patient anatomy and surgical tools, implants remain difficult to integrate due to tracking ch... | null | null |
Paper4612 | Automatic dataset shift identification to support safe deployment of medical imaging AI | [
"Mélanie Roschewitz",
"Raghav Mehta",
"Charles Jones",
"Ben Glocker"
] | https://papers.miccai.org/miccai-2025/0082-Paper4612.html | https://papers.miccai.org/miccai-2025/paper/4612_paper.pdf | 10.1007/978-3-032-04981-0_7 | https://rdcu.be/eHwV2 | null | [
"Body -> Breast",
"Body -> Eye",
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Machine Learning -> Other",
"Machine Learning -> Validation"
] | [
"https://github.com/biomedia-mira/shift_identification"
] | [
"https://bimcv.cipf.es/bimcv-projects/padchest/",
"https://github.com/Emory-HITI/EMBED_Open_Data/tree/main",
"https://www.adcis.net/en/third-party/messidor2/",
"https://www.kaggle.com/competitions/aptos2019-blindness-detection/data",
"https://www.kaggle.com/c/diabetic-retinopathy-detection/data"
] | 67-76 | @InProceedings{RosMél_Automatic_MICCAI2025,
author = { Roschewitz, Mélanie AND Mehta, Raghav AND Jones, Charles AND Glocker, Ben},
title = { { Automatic dataset shift identification to support safe deployment of medical imaging AI } },
booktitle = {proceedings of Medical Image Computing and Comp... | Shifts in data distribution can substantially harm the performance of clinical AI models and lead to misdiagnosis. Hence, various methods have been developed to detect the presence of such shifts at deployment time. However, root causes of dataset shifts are varied, and the choice of shift mitigation strategies highly ... | 2411.07940 | title_snapshot |
Paper1316 | Automatic Deep Deformable Registration using Domain Adaptation and Run-Time Optimisation | [
"Emilien Gadoux",
"Adrien Bartoli"
] | https://papers.miccai.org/miccai-2025/0083-Paper1316.html | https://papers.miccai.org/miccai-2025/paper/1316_paper.pdf | 10.1007/978-3-032-05114-1_7 | https://rdcu.be/eHw0X | https://papers.miccai.org/miccai-2025/supp/1316_supp.zip | [
"Body -> Abdomen",
"Modalities -> Endoscopy",
"Applications -> Image Registration",
"Applications -> Image-Guided Interventions",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization",
"Surgery -> Navigation"
] | [
"https://github.com/EmilienGad/ADeLiR.git"
] | [
"https://encov.ip.uca.fr/ab/code_and_datasets/datasets/llr_reg_evaluation_by_lus/index.php"
] | 65-74 | @InProceedings{GadEmi_Automatic_MICCAI2025,
author = { Gadoux, Emilien AND Bartoli, Adrien},
title = { { Automatic Deep Deformable Registration using Domain Adaptation and Run-Time Optimisation } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI ... | Augmented reality from preoperative 3D model registration is promising to assist navigation in minimally-invasive liver surgery. The current registration methods are either accurate, but require surgeon interactions to annotate anatomical landmarks, or are fully automatic, but inaccurate. We propose a two-step automati... | null | null |
Paper1847 | Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction | [
"Tao Chen",
"Chenhui Wang",
"Zhihao Chen",
"Hongming Shan"
] | https://papers.miccai.org/miccai-2025/0084-Paper1847.html | https://papers.miccai.org/miccai-2025/paper/1847_paper.pdf | 10.1007/978-3-032-04937-7_3 | https://rdcu.be/eHwMO | null | [
"Body -> Brain",
"Body -> Lung",
"Modalities -> CT / X-Ray",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/takimailto/AR-Seg"
] | [] | 24-34 | @InProceedings{CheTao_Autoregressive_MICCAI2025,
author = { Chen, Tao AND Wang, Chenhui AND Chen, Zhihao AND Shan, Hongming},
title = { { Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Interv... | While deep learning has significantly advanced medical image segmentation, most existing methods still struggle with handling complex anatomical regions. Cascaded or deep supervision-based approaches attempt to address this challenge through multi-scale feature learning but fail to establish sufficient inter-scale depe... | 2502.20784 | title_snapshot |
Paper0853 | AVDM: Controllable Adversarial Diffusion Model for Vessel-to-Volume Synthesis | [
"Jian Dai",
"Wanchen Liu",
"Honghao Cui",
"Xiao Liu",
"Jiajun Wang",
"Zhiji Zheng",
"Daoying Geng"
] | https://papers.miccai.org/miccai-2025/0085-Paper0853.html | https://papers.miccai.org/miccai-2025/paper/0853_paper.pdf | 10.1007/978-3-032-05325-1_8 | https://rdcu.be/eHxd3 | null | [
"Body -> Brain",
"Modalities -> MRI",
"Modalities -> MRI - Diffusion Imaging",
"Applications -> Computer Aided Diagnosis",
"Applications -> Image Segmentation",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning"
] | [] | [] | 78-87 | @InProceedings{DaiJia_AVDM_MICCAI2025,
author = { Dai, Jian AND Liu, Wanchen AND Cui, Honghao AND Liu, Xiao AND Wang, Jiajun AND Zheng, Zhiji AND Geng, Daoying},
title = { { AVDM: Controllable Adversarial Diffusion Model for Vessel-to-Volume Synthesis } },
booktitle = {proceedings of Medical Ima... | 3D blood vessel segmentation remains a critical yet challenging task in medical image analysis. The heterogeneity of clinical imaging protocols introduces substantial domain gaps, limiting the generalizability of supervised learning methods that rely on manually annotated pixel-level labels for individual datasets. Fur... | null | null |
Paper4304 | Aβ-PET Pattern Prediction via Graph Reconstruction-Aware Fusion (GRAF) of Functional and Structural Networks | [
"Haoyue Yuan",
"Yuxiao Liu",
"Feihong Liu",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2025/0086-Paper4304.html | https://papers.miccai.org/miccai-2025/paper/4304_paper.pdf | 10.1007/978-3-032-05162-2_8 | https://rdcu.be/eHc4f | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Brain Network Analysis"
] | [
"https://github.com/ninicassiel/GRAF"
] | [
"https://www.huashan.org.cn/pet/"
] | 76-86 | @InProceedings{YuaHao_AβPET_MICCAI2025,
author = { Yuan, Haoyue AND Liu, Yuxiao AND Liu, Feihong AND Shen, Dinggang},
title = { { Aβ-PET Pattern Prediction via Graph Reconstruction-Aware Fusion (GRAF) of Functional and Structural Networks } },
booktitle = {proceedings of Medical Image Computing ... | Alzheimer’s disease (AD) is characterized by abnormal amyloid-β (Aβ) deposition, which causes neural damage and cognitive decline. Aβ positron emission tomography (PET) serves as the gold standard for preclinical diagnosis of AD. However, practical limitations, including high costs, radiation exposure, and constrained ... | null | null |
Paper2868 | Background-Invariant Independence-Guided Multi-head Attention Network for Skin Lesion Classification | [
"Debasmit Roy",
"Srinjoy Dutta",
"Soham Bose",
"Friedhelm Schwenker",
"Ram Sarkar"
] | https://papers.miccai.org/miccai-2025/0087-Paper2868.html | https://papers.miccai.org/miccai-2025/paper/2868_paper.pdf | 10.1007/978-3-032-05169-1_4 | https://rdcu.be/eHw8e | null | [
"Body -> Skin",
"Modalities -> Photograph / Video",
"Applications -> Computer Aided Diagnosis"
] | [
"https://github.com/shb2908/BIIGMA-Net"
] | [
"https://challenge.isic-archive.com/data/#2017",
"https://challenge.isic-archive.com/data/#2018",
"https://challenge.isic-archive.com/data/#2019"
] | 34-44 | @InProceedings{RoyDeb_BackgroundInvariant_MICCAI2025,
author = { Roy, Debasmit AND Dutta, Srinjoy AND Bose, Soham AND Schwenker, Friedhelm AND Sarkar, Ram},
title = { { Background-Invariant Independence-Guided Multi-head Attention Network for Skin Lesion Classification } },
booktitle = {proceedi... | Biomedical image classification faces several adversarial challenges, including occlusions from artifacts, variations in tissue pigmentation, and class imbalance, which hinder model generalization. Existing attention mechanisms enhance region localization but often introduce redundant dependencies across attention head... | null | null |
Paper3305 | BaMCo: Balanced Multimodal Contrastive Learning for Knowledge-Driven Medical VQA | [
"Ziya Ata Yazıcı",
"Hazım Kemal Ekenel"
] | https://papers.miccai.org/miccai-2025/0088-Paper3305.html | https://papers.miccai.org/miccai-2025/paper/3305_paper.pdf | 10.1007/978-3-032-04981-0_8 | https://rdcu.be/eHwV3 | null | [
"Body -> Brain",
"Body -> other",
"Modalities -> CT / X-Ray",
"Modalities -> Microscopy",
"Modalities -> Photograph / Video",
"Applications -> Computer Aided Diagnosis",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models"
] | [
"https://github.com/yaziciz/BaMCo"
] | [
"https://huggingface.co/datasets/BoKelvin/SLAKE",
"https://huggingface.co/datasets/flaviagiammarino/vqa-rad",
"https://huggingface.co/datasets/flaviagiammarino/path-vqa"
] | 77-87 | @InProceedings{YazZiy_BaMCo_MICCAI2025,
author = { Yazıcı, Ziya Ata AND Ekenel, Hazım Kemal},
title = { { BaMCo: Balanced Multimodal Contrastive Learning for Knowledge-Driven Medical VQA } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
... | Medical Visual Question Answering enables large language models to answer questions related to clinical images. While domain-specific LLMs are capable of strong reasoning, their development can be costly. In contrast, general-purpose models are more efficient, but often lack deep understanding. Previous research has sh... | null | null |
Paper4914 | Bayesian Transformers and Higher-Order Graph Matching for Cell Tracking in Serial Tissue Sections | [
"Mostafa Karami",
"Sahand Hamzehei",
"David Arce",
"Gianna Raimondi",
"Linnaea Ostroff",
"Sheida Nabavi"
] | https://papers.miccai.org/miccai-2025/0089-Paper4914.html | https://papers.miccai.org/miccai-2025/paper/4914_paper.pdf | 10.1007/978-3-032-05162-2_9 | https://rdcu.be/eHc4g | null | [
"Body -> other",
"Modalities -> Microscopy",
"Applications -> Image Registration",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning",
"Machine Learning -> Uncertainty"
] | [] | [] | 87-97 | @InProceedings{KarMos_Bayesian_MICCAI2025,
author = { Karami, Mostafa AND Hamzehei, Sahand AND Arce, David AND Raimondi, Gianna AND Ostroff, Linnaea AND Nabavi, Sheida},
title = { { Bayesian Transformers and Higher-Order Graph Matching for Cell Tracking in Serial Tissue Sections } },
booktitle =... | Reliable 3D reconstruction of tissue architecture from sequential 2D multiplex images is challenging due to the noise and distortions introduced by ultrathin (50 nm) slicing and complex alignment procedures. Conventional cell tracking methods often fail under such conditions, resulting in inaccurate linkage of cells ac... | null | null |
Paper0645 | BayeSMM: Robust Deep Combined Computing Tackling Heavy-tailed Distribution in Medical Images | [
"Yuanye Liu",
"Ruoxuan Zhen",
"Shangqi Gao",
"Xinzhe Luo",
"Xin Gao",
"Qingchao Chen",
"Xiahai Zhuang"
] | https://papers.miccai.org/miccai-2025/0090-Paper0645.html | https://papers.miccai.org/miccai-2025/paper/0645_paper.pdf | 10.1007/978-3-032-05169-1_5 | https://rdcu.be/eHw8f | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Image Registration",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/HenryLau7/BayeSMM"
] | [
"https://zmiclab.github.io/zxh/0/mscmrseg19/"
] | 45-54 | @InProceedings{LiuYua_BayeSMM_MICCAI2025,
author = { Liu, Yuanye AND Zhen, Ruoxuan AND Gao, Shangqi AND Luo, Xinzhe AND Gao, Xin AND Chen, Qingchao AND Zhuang, Xiahai},
title = { { BayeSMM: Robust Deep Combined Computing Tackling Heavy-tailed Distribution in Medical Images } },
booktitle = {proc... | Abnormal structures in multi-modality medical images often lead to heterogeneous heavy-tailed distributions. However, traditional models, especially those relying on Gaussian distributions, struggle to effectively capture these outliers. To address this, we propose BayeSMM, a novel framework that leverages Student’s $t... | null | null |
Paper1649 | BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement | [
"Qian Li",
"Feng Liu",
"Shuojue Yang",
"Daiyun Shen",
"Yueming Jin"
] | https://papers.miccai.org/miccai-2025/0091-Paper1649.html | https://papers.miccai.org/miccai-2025/paper/1649_paper.pdf | 10.1007/978-3-032-05127-1_8 | https://rdcu.be/eHw3f | null | [
"Body -> Abdomen",
"Modalities -> Endoscopy",
"Applications -> Computational Anatomy and Physiology",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/jinlab-imvr/BCRNet"
] | [
"https://github.com/PJLallen/D2GPLand",
"https://p2ilf.grand-challenge.org/"
] | 77-87 | @InProceedings{LiQia_BCRNet_MICCAI2025,
author = { Li, Qian AND Liu, Feng AND Yang, Shuojue AND Shen, Daiyun AND Jin, Yueming},
title = { { BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement } },
booktitle = {proceedings of Medical Image Computing and ... | Laparoscopic liver surgery, while minimally invasive, poses significant challenges in accurately identifying critical anatomical structures. Augmented reality (AR) systems, integrating MRI/CT with laparoscopic images based on 2D-3D registration, offer a promising solution for enhancing surgical navigation. A vital aspe... | 2506.15279 | title_snapshot |
Paper1683 | BenchReAD: A systematic benchmark for retinal anomaly detection | [
"Chenyu Lian",
"Hong-Yu Zhou",
"Zhanli Hu",
"Jing Qin"
] | https://papers.miccai.org/miccai-2025/0092-Paper1683.html | https://papers.miccai.org/miccai-2025/paper/1683_paper.pdf | 10.1007/978-3-032-04937-7_4 | https://rdcu.be/eHwMP | null | [
"Body -> Eye",
"Modalities -> Photograph / Video",
"Applications -> Anomaly Detection",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/DopamineLcy/BenchReAD"
] | [] | 35-45 | @InProceedings{LiaChe_BenchReAD_MICCAI2025,
author = { Lian, Chenyu AND Zhou, Hong-Yu AND Hu, Zhanli AND Qin, Jing},
title = { { BenchReAD: A systematic benchmark for retinal anomaly detection } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2... | Retinal anomaly detection plays a pivotal role in screening ocular and systemic diseases. Despite its significance, progress in the field has been hindered by the absence of a comprehensive and publicly available benchmark, which is essential for the fair evaluation and advancement of methodologies. Due to this limitat... | 2507.10492 | title_snapshot |
Paper4043 | Beyond Shadows: Learning Physics-inspired Ultrasound Confidence Maps from Sparse Annotations | [
"Matteo Ronchetti",
"Rüdiger Göbl",
"Bugra Yesilkaynak",
"Oliver Zettinig",
"Nassir Navab"
] | https://papers.miccai.org/miccai-2025/0093-Paper4043.html | https://papers.miccai.org/miccai-2025/paper/4043_paper.pdf | 10.1007/978-3-032-05169-1_6 | https://rdcu.be/eHw8g | null | [
"Body -> Abdomen",
"Body -> other",
"Modalities -> Ultrasound",
"Applications -> Image Registration",
"Applications -> Image Segmentation",
"Applications -> Other"
] | [] | [] | 55-64 | @InProceedings{RonMat_Beyond_MICCAI2025,
author = { Ronchetti, Matteo AND Göbl, Rüdiger AND Yesilkaynak, Bugra AND Zettinig, Oliver AND Navab, Nassir},
title = { { Beyond Shadows: Learning Physics-inspired Ultrasound Confidence Maps from Sparse Annotations } },
booktitle = {proceedings of Medica... | This paper introduces a novel user-centered approach for generating confidence maps in ultrasound imaging. Existing methods, relying on simplified models, often fail to account for the full range of ultrasound artifacts and are limited by arbitrary boundary conditions, making frame-to-frame comparisons challenging. Our... | null | null |
Paper3881 | Bias and Generalizability of Foundation Models across Datasets in Breast Mammography | [
"Elodie Germani",
"Ilayda Selin-Türk",
"Fatima Zeineddine",
"Charbel Mourad",
"Shadi Albarqouni"
] | https://papers.miccai.org/miccai-2025/0094-Paper3881.html | https://papers.miccai.org/miccai-2025/paper/3881_paper.pdf | 10.1007/978-3-032-05185-1_3 | https://rdcu.be/eHxbR | null | [
"Body -> Breast",
"Modalities -> CT / X-Ray",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Algorithmic Fairness",
"Machine Learning -> Domain Adaptation / Harmonization",
"Machine Learning -> Foundation Models"
] | [] | [] | 24-34 | @InProceedings{GerElo_Bias_MICCAI2025,
author = { Germani, Elodie AND Selin-Türk, Ilayda AND Zeineddine, Fatima AND Mourad, Charbel AND Albarqouni, Shadi},
title = { { Bias and Generalizability of Foundation Models across Datasets in Breast Mammography } },
booktitle = {proceedings of Medical Im... | Over the past decades, computer-aided diagnosis tools for breast cancer have been developed to enhance screening procedures, yet their clinical adoption remains challenged by data variability and inherent biases. Although foundation models (FMs) have recently demonstrated impressive generalizability and transfer learni... | 2505.10579 | title_snapshot |
Paper3276 | BiasICL: In-Context Learning and Demographic Biases of Vision Language Models | [
"Sonnet Xu",
"Joseph D. Janizek",
"Yixing Jiang",
"Roxana Daneshjou"
] | https://papers.miccai.org/miccai-2025/0095-Paper3276.html | https://papers.miccai.org/miccai-2025/paper/3276_paper.pdf | 10.1007/978-3-032-04981-0_9 | https://rdcu.be/eHwV4 | null | [
"Modalities -> CT / X-Ray",
"Modalities -> Photograph / Video",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Algorithmic Fairness",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models"
] | [
"https://github.com/DaneshjouLab/BiasICL"
] | [
"https://ddi-dataset.github.io/",
"https://aimi.stanford.edu/datasets/chexpert-chest-x-rays"
] | 88-97 | @InProceedings{XuSon_BiasICL_MICCAI2025,
author = { Xu, Sonnet AND Janizek, Joseph D. AND Jiang, Yixing AND Daneshjou, Roxana},
title = { { BiasICL: In-Context Learning and Demographic Biases of Vision Language Models } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted... | Vision language models (VLMs) show promise in medical diagnosis, but their performance across demographic subgroups when using in-context learning (ICL) remains poorly understood. We examine how the demographic composition of demonstration examples affects VLM performance in two medical imaging tasks: skin lesion malig... | 2503.02334 | title_snapshot |
Paper1799 | BiMSRec: A Progressive Image Reconstruction Framework for Medical Image Fusion Guided by Multi-Scale Deformation Fields | [
"Nuoer Long",
"Kaiwen Yang",
"Xinyu Xie",
"Zitong Yu",
"Tao Tan",
"Yue Sun"
] | https://papers.miccai.org/miccai-2025/0096-Paper1799.html | https://papers.miccai.org/miccai-2025/paper/1799_paper.pdf | 10.1007/978-3-032-04937-7_5 | https://rdcu.be/eHwMQ | null | [
"Body -> Brain",
"Applications -> Image Registration",
"Machine Learning -> Deep Learning"
] | [] | [] | 46-55 | @InProceedings{LonNuo_BiMSRec_MICCAI2025,
author = { Long, Nuoer AND Yang, Kaiwen AND Xie, Xinyu AND Yu, Zitong AND Tan, Tao AND Sun, Yue},
title = { { BiMSRec: A Progressive Image Reconstruction Framework for Medical Image Fusion Guided by Multi-Scale Deformation Fields } },
booktitle = {procee... | Traditional multi-modal medical image fusion methods typically employ a hierarchical feature fusion strategy. However, due to inconsistencies among features at different scales, these approaches often introduce unanticipated deformations during the fusion process. Such deformations accumulate through successive registr... | null | null |
Paper1852 | Bio2Vol: Adapting 2D Biomedical Foundation Models for Volumetric Medical Image Segmentation | [
"Jiaxin Zhuang",
"Linshan Wu",
"Xuefeng Ni",
"Xi Wang",
"Liansheng Wang",
"Hao Chen"
] | https://papers.miccai.org/miccai-2025/0097-Paper1852.html | https://papers.miccai.org/miccai-2025/paper/1852_paper.pdf | 10.1007/978-3-032-04978-0_3 | https://rdcu.be/eHdSl | null | [
"Body -> Abdomen",
"Modalities -> CT / X-Ray",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models"
] | [] | [] | 24-34 | @InProceedings{ZhuJia_Bio2Vol_MICCAI2025,
author = { Zhuang, Jiaxin AND Wu, Linshan AND Ni, Xuefeng AND Wang, Xi AND Wang, Liansheng AND Chen, Hao},
title = { { Bio2Vol: Adapting 2D Biomedical Foundation Models for Volumetric Medical Image Segmentation } },
booktitle = {proceedings of Medical Im... | 2D biomedical foundation models (FM) have demonstrated remarkable capabilities in 2D medical image segmentation across various modalities, with text-prompted approaches offering scalable analysis that facilitate integration with LLMs and clinical application. Adapting these models for 3D medical image segmentation can ... | null | null |
Paper0768 | BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQA | [
"Zhengyang Ji",
"Shang Gao",
"Li Liu",
"Yifan Jia",
"Yutao Yue"
] | https://papers.miccai.org/miccai-2025/0098-Paper0768.html | https://papers.miccai.org/miccai-2025/paper/0768_paper.pdf | 10.1007/978-3-032-05127-1_9 | https://rdcu.be/eHw3g | null | [
"Body -> other",
"Modalities -> Other",
"Applications -> Computer Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Surgery -> Data Science"
] | [
"https://github.com/jzy-123/BioD2C"
] | [
"https://osf.io/89kps/",
"https://www.med-vqa.com/slake/",
"https://github.com/UCSD-AI4H/PathVQA",
"https://huggingface.co/datasets/jzyang/BioVGQ"
] | 88-97 | @InProceedings{JiZhe_BioD2C_MICCAI2025,
author = { Ji, Zhengyang AND Gao, Shang AND Liu, Li AND Jia, Yifan AND Yue, Yutao},
title = { { BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQA } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted... | Biomedical visual question answering (VQA) has been widely studied and has demonstrated significant application value and potential in fields such as assistive medical diagnosis. Despite their success, current biomedical VQA models perform multimodal information interaction only at the model level within large language... | 2503.02476 | title_snapshot |
Paper2356 | Bipartite Patient-Modality Graph Learning with Event-Conditional Modelling of Censoring for Cancer Survival Prediction | [
"Hailin Yue",
"Hulin Kuang",
"Jin Liu",
"Junjian Li",
"Lanlan Wang",
"Mengshen He",
"Jianxin Wang"
] | https://papers.miccai.org/miccai-2025/0099-Paper2356.html | https://papers.miccai.org/miccai-2025/paper/2356_paper.pdf | 10.1007/978-3-032-05162-2_10 | https://rdcu.be/eHc4h | null | [
"Body -> Lung",
"Modalities -> Microscopy",
"Applications -> Computational (Integrative) Pathology",
"Applications -> Computer Aided Diagnosis",
"Applications -> Integration of Imaging with Non-Imaging Biomarkers",
"Machine Learning -> Deep Learning",
"Machine Learning -> Uncertainty"
] | [
"https://github.com/yuehailin/CenSurv"
] | [] | 98-108 | @InProceedings{YueHai_Bipartite_MICCAI2025,
author = { Yue, Hailin AND Kuang, Hulin AND Liu, Jin AND Li, Junjian AND Wang, Lanlan AND He, Mengshen AND Wang, Jianxin},
title = { { Bipartite Patient-Modality Graph Learning with Event-Conditional Modelling of Censoring for Cancer Survival Prediction } },
... | Accurately predicting the survival of cancer patients is crucial for personalized treatment. However, existing studies focus solely on the relationships between samples with known survival risks, ignoring the value of censored samples, which is inevitable in clinical practice. Furthermore, these studies may suffer perf... | 2507.16363 | title_snapshot |
Paper4637 | BiSCoT: Behavior-Informed Subgroup-Consistent Connectome Template for Interpretable Brain Network Analysis | [
"Zijian Chen",
"Stefen Beeler-Duden",
"Sophie Lawson",
"Zachary Jacokes",
"John Darrell Van Horn",
"Kevin A. Pelphrey",
"Archana Venkataraman"
] | https://papers.miccai.org/miccai-2025/0100-Paper4637.html | https://papers.miccai.org/miccai-2025/paper/4637_paper.pdf | 10.1007/978-3-032-05162-2_11 | https://rdcu.be/eHc4i | null | [
"Body -> Brain",
"Modalities -> MRI - Functional MRI",
"Applications -> Brain Network Analysis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/zijianch/biscot"
] | [] | 109-119 | @InProceedings{CheZij_BiSCoT_MICCAI2025,
author = { Chen, Zijian AND Beeler-Duden, Stefen AND Lawson, Sophie AND Jacokes, Zachary AND Van Horn, John Darrell AND Pelphrey, Kevin A. AND Venkataraman, Archana},
title = { { BiSCoT: Behavior-Informed Subgroup-Consistent Connectome Template for Interpretable ... | We propose a graph information compression framework, called Behavior-Informed Subgroup-consistent Connectome Template (BISCoT), that learns interpretable functional subnetworks from restingstate fMRI (rs-fMRI) connectivity, which simultaneously capture the heterogeneity of a diverse patient cohort. BISCoT uses multidi... | null | null |
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