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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Paper0829 | 3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images | [
"Alina F. Dima",
"Veronika A. Zimmer",
"Martin J. Menten",
"Hongwei Bran Li",
"Markus Graf",
"Tristan Lemke",
"Philipp Raffler",
"Robert Graf",
"Jan S. Kirschke",
"Rickmer Braren",
"Daniel Rueckert"
] | https://conferences.miccai.org/2023/papers/001-Paper0829.html | null | 10.1007/978-3-031-43907-0_14 | https://rdcu.be/dnwcc | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Clinical applications - Vascular",
"Image Segmentation",
"Modalities - CT"
] | [
"https://github.com/alinafdima/3Dseg-mip-depth"
] | [] | null | null | Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is being actively investigated in existing works, mostly in deep learning approaches. However, training 3D deep networks requires large amounts of... | 2309.08481 | title_snapshot |
Paper1655 | 3D Dental Mesh Segmentation Using Semantics-Based Feature Learning with Graph-Transformer | [
"Fan Duan",
"Li Chen"
] | https://conferences.miccai.org/2023/papers/002-Paper1655.html | null | 10.1007/978-3-031-43990-2_43 | https://rdcu.be/dnwLY | null | [
"Clinical applications - Musculoskeletal",
"Machine Learning - Attention models",
"Machine Learning - Data Efficient Learning",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - other"
] | [
"https://github.com/df-boy/SGTNet"
] | [] | null | null | Accurate segmentation of digital 3D dental mesh plays a crucial role in various specialized applications within oral medicine. While certain deep learning-based methods have been explored for dental mesh segmentation, the current quality of segmentation fails to meet clinical requirements. This limitation can be attrib... | null | null |
Paper1307 | 3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks | [
"Heng Cai",
"Lei Qi",
"Qian Yu",
"Yinghuan Shi",
"Yang Gao"
] | https://conferences.miccai.org/2023/papers/003-Paper1307.html | null | 10.1007/978-3-031-43898-1_59 | https://rdcu.be/dnwBT | null | [
"Image Segmentation",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning"
] | [
"https://github.com/HengCai-NJU/3D2DCT"
] | [] | null | null | Medical image segmentation typically necessitates a large and precisely annotated dataset. However, obtaining pixel-wise annotation is a labor-intensive task that requires significant effort from domain experts, making it challenging to obtain in practical clinical scenarios. In such situations, reducing the amount of ... | 2307.16256 | title_snapshot |
Paper2386 | 3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers | [
"Omkar Thawakar",
"Rao Muhammad Anwer",
"Jorma Laaksonen",
"Orly Reiner",
"Mubarak Shah",
"Fahad Shahbaz Khan"
] | https://conferences.miccai.org/2023/papers/004-Paper2386.html | null | 10.1007/978-3-031-43993-3_59 | https://rdcu.be/dnwN4 | null | [
"Image Segmentation",
"Modalities - Microscopy"
] | [
"https://github.com/OmkarThawakar/STT-UNET"
] | [] | null | null | Accurate 3D mitochondria instance segmentation in electron microscopy (EM) is a challenging problem and serves as a prerequisite to empirically analyze their distributions and morphology. Most existing approaches employ 3D convolutions to obtain representative features. However, these convolution-based approaches strug... | 2303.12073 | title_snapshot |
Paper2035 | 3D Teeth Reconstruction from Panoramic Radiographs using Neural Implicit Functions | [
"Sihwa Park",
"Seongjun Kim",
"In-Seok Song",
"Seung Jun Baek"
] | https://conferences.miccai.org/2023/papers/005-Paper2035.html | null | 10.1007/978-3-031-43999-5_36 | https://rdcu.be/dnwwP | null | [
"Image Reconstruction",
"Image Segmentation",
"Machine Learning - Other",
"Modalities - CT",
"Modalities - other"
] | [] | [] | null | null | Panoramic radiography is a widely used imaging modality in dental practice and research. However, it only provides flattened 2D images, which limits the detailed assessment of dental structures. In this paper, we propose Occudent, a framework for 3D teeth reconstruction from panoramic radiographs using neural implicit ... | 2311.16524 | title_snapshot |
Paper1808 | A Closed-form Solution to Electromagnetic Sensor Based Intraoperative Limb Length Measurement in Total Hip Arthroplasty | [
"Tiancheng Li",
"Yang Song",
"Peter Walker",
"Kai Pan",
"Victor A. van de Graaf",
"Liang Zhao",
"Shoudong Huang"
] | https://conferences.miccai.org/2023/papers/006-Paper1808.html | null | 10.1007/978-3-031-43996-4_35 | https://rdcu.be/dnwPe | null | [
"Rigorous Evaluations of Methodology in Clinical Workflows",
"Clinical applications - Musculoskeletal",
"Surgical Visualization and Mixed/Augmented/Virtual Reality"
] | [] | [] | null | null | Total hip arthroplasty (THA) is an orthopaedic surgery to replace the diseased ball and socket of the hip joint with artificial implants. Achieving appropriate leg length and offset in THA is critical to avoid instability, leg length discrepancies, persistent pain, or early implant failure. This paper provides the firs... | null | null |
Paper0976 | A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy | [
"Haoran Dou",
"Nishant Ravikumar",
"Alejandro F. Frangi"
] | https://conferences.miccai.org/2023/papers/007-Paper0976.html | null | 10.1007/978-3-031-43990-2_14 | https://rdcu.be/dnwLo | null | [
"Clinical applications - Cardiac",
"Computational Anatomy and Physiology",
"Modalities - other"
] | [] | [] | null | null | Generating virtual populations (VPs) of anatomy is essential for conducting in-silico trials of medical devices. Typically, the generated VP should capture sufficient variability while remaining plausible, and should reflect specific characteristics and patient demographics observed in real populations. It is desirable... | 2306.14680 | title_snapshot |
Paper3288 | A coupled-mechanisms modelling framework for neurodegeneration | [
"Tiantian He",
"Elinor Thompson",
"Anna Schroder",
"Neil P. Oxtoby",
"Ahmed Abdulaal",
"Frederik Barkhof",
"Daniel C. Alexander"
] | https://conferences.miccai.org/2023/papers/008-Paper3288.html | null | 10.1007/978-3-031-43993-3_45 | https://rdcu.be/dnwNQ | null | [
"Clinical applications - Neuroimaging - Others",
"Clinical applications - Neuroimaging - DWI and Tractography",
"Clinical applications - Neuroimaging - Functional Brain Networks",
"Machine Learning - Uncertainty"
] | [] | [
"https://adni.loni.usc.edu/",
"https://www.humanconnectome.org/study/hcp-young-adult",
"https://portal.conp.ca/dataset?id=projects/mica-mics#"
] | null | null | Computational models of neurodegeneration aim to emulate the evolving pattern of pathology in the brain during neurodegenerative disease, such as Alzheimer’s disease. Previous studies have made specific choices on the mechanisms of pathology production and diffusion, or assume that all the subjects lie on the same dise... | 2308.05536 | title_snapshot |
Paper1168 | A denoised Mean Teacher for domain adaptive point cloud registration | [
"Alexander Bigalke",
"Mattias P. Heinrich"
] | https://conferences.miccai.org/2023/papers/009-Paper1168.html | null | 10.1007/978-3-031-43999-5_63 | https://rdcu.be/dnwxg | null | [
"Image Registration",
"Machine Learning - Transfer learning"
] | [
"https://github.com/multimodallearning/denoised_mt_pcd_reg",
"https://med.emory.edu/departments/radiation-oncology/research-laboratories/deformable-image-registration/downloads-and-reference-data/copdgene.html"
] | [
"https://github.com/uncbiag/robot",
"https://med.emory.edu/departments/radiation-oncology/research-laboratories/deformable-image-registration/downloads-and-reference-data/copdgene.html"
] | null | null | Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation but is limited by the inefficacy of unsupervised learning with similarity metrics. Supervised training on synthetic deformations is an alternative but, in turn, suffers from the... | 2306.14749 | title_snapshot |
Paper1779 | A flexible framework for simulating and evaluating biases in deep learning-based medical image analysis | [
"Emma A. M. Stanley",
"Matthias Wilms",
"Nils D. Forkert"
] | https://conferences.miccai.org/2023/papers/010-Paper1779.html | null | 10.1007/978-3-031-43895-0_46 | https://rdcu.be/dnwyZ | null | [
"Machine Learning - Model Generalizability / Federated Learning",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Other",
"Modalities - MRI"
] | [
"https://github.com/estanley16/SimBA"
] | [] | null | null | Despite the remarkable advances in deep learning for medical image analysis, it has become evident that biases in datasets used for training such models pose considerable challenges for a clinical deployment, including fairness and domain generalization issues. Although the development of bias mitigation techniques has... | null | null |
Paper0286 | A General Stitching Solution for Whole-Brain 3D Nuclei Instance Segmentation from Microscopy Images | [
"Ziquan Wei",
"Tingting Dan",
"Jiaqi Ding",
"Mustafa Dere",
"Guorong Wu"
] | https://conferences.miccai.org/2023/papers/011-Paper0286.html | null | 10.1007/978-3-031-43901-8_5 | https://rdcu.be/dnwCF | null | [
"Image Segmentation",
"Modalities - Microscopy"
] | [] | [] | null | null | High-throughput 3D nuclei instance segmentation (NIS) is critical to understanding the complex structure and function of individual cells and their interactions within the larger tissue environment in the brain. Despite the significant progress in achieving accurate NIS within small image stacks using cutting-edge mach... | null | null |
Paper1873 | A Model-Agnostic Framework for Universal Anomaly Detection of Multi-Organ and Multi-Modal Images | [
"Yinghao Zhang",
"Donghuan Lu",
"Munan Ning",
"Liansheng Wang",
"Dong Wei",
"Yefeng Zheng"
] | https://conferences.miccai.org/2023/papers/012-Paper1873.html | null | 10.1007/978-3-031-43898-1_23 | https://rdcu.be/dnwAU | null | [
"Machine Learning - Other",
"Computer Aided Diagnosis"
] | [
"https://github.com/lianjizhe/MADDR_code"
] | [] | null | null | The recent success of deep learning relies heavily on the large amount of labeled data. However, acquiring manually annotated symptomatic medical images is notoriously time-consuming and laborious, especially for rare or new diseases. In contrast, normal images from symptom-free healthy subjects without the need of man... | null | null |
Paper1912 | A Modulatory Elongated Model for Delineating Retinal Microvasculature in OCTA Images | [
"Mohsin Challoob",
"Yongsheng Gao",
"Andrew Busch",
"Weichuan Zhang"
] | https://conferences.miccai.org/2023/papers/013-Paper1912.html | null | 10.1007/978-3-031-43990-2_67 | https://rdcu.be/dnwMr | null | [
"Clinical applications - Ophthalmology",
"Clinical applications - Vascular"
] | [] | [] | null | null | Robust delineation of retinal microvasculature in optical coherence tomography angiography (OCTA) images remains a challenging task, particularly in handling the weak continuity of vessels, low visibility of capillaries, and significant noise interferences. This paper introduces a modulatory elongated model to overcome... | null | null |
Paper0643 | A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images | [
"Yudong Zhang",
"Ge Yang"
] | https://conferences.miccai.org/2023/papers/014-Paper0643.html | null | 10.1007/978-3-031-43993-3_49 | https://rdcu.be/dnwNU | null | [
"Modalities - Microscopy",
"Machine Learning - Attention models",
"Modalities - Video"
] | [
"https://github.com/imzhangyd/MoTT.git"
] | [
"http://bioimageanalysis.org/track/"
] | null | null | Single particle tracking is an important image analysis technique widely used in biomedical sciences to follow the movement of subcellular structures, which typically appear as individual particles in fluorescence microscopy images. In practice, the low signal-to-noise ratio (SNR) of fluorescence microscopy images as w... | null | null |
Paper2184 | A Multimodal Disease Progression Model for Genetic Associations with Disease Dynamics | [
"Nemo Fournier",
"Stanley Durrleman"
] | https://conferences.miccai.org/2023/papers/015-Paper2184.html | null | 10.1007/978-3-031-43904-9_58 | https://rdcu.be/dnwH4 | null | [
"Treatment Response and Outcome/Disease Prediction",
"Integration of Imaging with Non-Imaging Biomarkers"
] | [] | [] | null | null | We introduce a disease progression model suited for neurodegenerative pathologies that allows to model associations between covariates and dynamic features of the disease course. We establish a statistical framework and implement an algorithm for its estimation. We show that the model is reliable and can provide uncert... | null | null |
Paper1115 | A Multi-Task Method for Immunofixation Electrophoresis Image Classification | [
"Yi Shi",
"Rui-Xiang Li",
"Wen-Qi Shao",
"Xin-Cen Duan",
"Han-Jia Ye",
"De-Chuan Zhan",
"Bai-Shen Pan",
"Bei-Li Wang",
"Wei Guo",
"Yuan Jiang"
] | https://conferences.miccai.org/2023/papers/016-Paper1115.html | null | 10.1007/978-3-031-43987-2_15 | https://rdcu.be/dnwJx | null | [
"Computer Aided Diagnosis",
"Modalities - other"
] | [
"https://github.com/shiy19/IFE-classification"
] | [] | null | null | In the field of plasma cell disorders diagnosis, the detection of abnormal monoclonal (M) proteins through Immunofixation Electrophoresis (IFE) is a widely accepted practice. However, the classification of IFE images into nine distinct categories is a complex task due to the significant class imbalance problem. To addr... | null | null |
Paper2273 | A Multi-Task Network for Anatomy Identification in Endoscopic Pituitary Surgery | [
"Adrito Das",
"Danyal Z. Khan",
"Simon C. Williams",
"John G. Hanrahan",
"Anouk Borg",
"Neil L. Dorward",
"Sophia Bano",
"Hani J. Marcus",
"Danail Stoyanov"
] | https://conferences.miccai.org/2023/papers/017-Paper2273.html | null | 10.1007/978-3-031-43996-4_45 | https://rdcu.be/dnwPq | null | [
"Surgical Data Science",
"Image Segmentation",
"Surgical Scene Understanding",
"Surgical Skill and Work Flow Analysis"
] | [
"https://github.com/dreets/pitnet-anat-public"
] | [] | null | null | Pituitary tumours are in an anatomically dense region of the body, and often distort or encase the surrounding critical structures. This, in combination with anatomical variations and limitations imposed by endoscope technology, makes intra-operative identification and protection of these structures challenging. Advanc... | null | null |
Paper1457 | A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images | [
"Yan-Jie Zhou",
"Wei Liu",
"Yuan Gao",
"Jing Xu",
"Le Lu",
"Yuping Duan",
"Hao Cheng",
"Na Jin",
"Xiaoyong Man",
"Shuang Zhao",
"Yu Wang"
] | https://conferences.miccai.org/2023/papers/018-Paper1457.html | null | 10.1007/978-3-031-43987-2_20 | https://rdcu.be/dnwJC | null | [
"Computer Aided Diagnosis",
"Clinical applications - Dermatology"
] | [] | [] | null | null | Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients. However, most of the existing methods overlook the essential domain knowledge required for skin disease diagnosis. A novel multi-task model, namely DermImitForm... | 2307.08308 | title_snapshot |
Paper0813 | A Novel Video-CTU Registration Method with Structural Point Similarity for FURS Navigation | [
"Mingxian Yang",
"Yinran Chen",
"Bei Li",
"Zhiyuan Liu",
"Song Zheng",
"Jianhui Chen",
"Xiongbiao Luo"
] | https://conferences.miccai.org/2023/papers/019-Paper0813.html | null | 10.1007/978-3-031-43996-4_12 | https://rdcu.be/dnwOM | null | [
"Image-Guided Interventions and Surgery",
"Modalities - CT",
"Modalities - Video",
"Surgical Data Science"
] | [] | [] | null | null | Flexible ureteroscopy (FURS) navigation remains challenging since ureteroscopic images are poor quality with artifacts such as water and floating matters, leading to a difficulty in directly registering these images to preoperative images. This paper presents a novel 2D-3D registration method with structure point simil... | null | null |
Paper2659 | A One-class Variational Autoencoder (OCVAE) cascade for classifying atypical bone marrow cell sub-types | [
"Jonathan Tarquino",
"Jhonathan Rodríguez",
"Charlems Alvarez-Jimenez",
"Eduardo Romero"
] | https://conferences.miccai.org/2023/papers/020-Paper2659.html | null | 10.1007/978-3-031-43987-2_70 | https://rdcu.be/dnwKt | null | [
"Computational (Integrative) Pathology",
"Clinical applications - Oncology",
"Computer Aided Diagnosis",
"Modalities - Histopathology",
"Modalities - Microscopy"
] | [] | [
"https://doi.org/10.7937/TCIA.AXH3-T579"
] | null | null | Atypical bone marrow (BM) cell-subtype characterization defines the diagnosis and follow up of different hematologic disorders. However, this process is basically a visual task, which is prone to inter- and intra-observer variability. The presented work introduces a new application of one-class variational autoencoders... | null | null |
Paper2349 | A Patient-Specific Self-supervised Model for Automatic X-ray/CT Registration | [
"Baochang Zhang",
"Shahrooz Faghihroohi",
"Mohammad Farid Azampour",
"Shuting Liu",
"Reza Ghotbi",
"Heribert Schunkert",
"Nassir Navab"
] | https://conferences.miccai.org/2023/papers/021-Paper2349.html | null | 10.1007/978-3-031-43996-4_49 | https://rdcu.be/dnwPu | null | [
"Image-Guided Interventions and Surgery",
"Image Registration",
"Modalities - CT"
] | [
"https://github.com/BaochangZhang/PSSS_registration"
] | [] | null | null | The accurate estimation of X-ray source pose in relation to pre-operative images is crucial for minimally invasive procedures. However, existing deep learning-based automatic registration methods often have one or some limitations, including heavy reliance on subsequent conventional refinement steps, requiring manual a... | null | null |
Paper3224 | A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications | [
"Matteo Pennisi",
"Federica Proietto Salanitri",
"Giovanni Bellitto",
"Simone Palazzo",
"Ulas Bagci",
"Concetto Spampinato"
] | https://conferences.miccai.org/2023/papers/022-Paper3224.html | null | 10.1007/978-3-031-43898-1_41 | https://rdcu.be/dnwBB | null | [
"Machine Learning - Other",
"Clinical applications - Lung",
"Clinical applications - Ophthalmology"
] | [
"https://github.com/perceivelab/PLAN"
] | [] | null | null | Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using GANs as a proxy for data sharing is not a safe solution, as they tend to embed near-duplicates of real samples in the latent space. Recent wor... | 2307.02984 | title_snapshot |
Paper0753 | A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging | [
"Zheyao Gao",
"Yuanye Liu",
"Fuping Wu",
"Nannan Shi",
"Yuxin Shi",
"Xiahai Zhuang"
] | https://conferences.miccai.org/2023/papers/023-Paper0753.html | null | 10.1007/978-3-031-43904-9_18 | https://rdcu.be/dnwGV | null | [
"Computer Aided Diagnosis",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Uncertainty"
] | [
"https://github.com/key1589745/Multi-view_liver"
] | [] | null | null | Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnetic resonance imaging (MRI) usually take a sub-region of the liver as input, which could miss critical information. To explore richer represe... | 2306.12054 | title_snapshot |
Paper1104 | A Semantic-guided and Knowledge-based Generative Framework for Orthodontic Visual Outcome Preview | [
"Yizhou Chen",
"Xiaojun Chen"
] | https://conferences.miccai.org/2023/papers/024-Paper1104.html | null | 10.1007/978-3-031-43987-2_14 | https://rdcu.be/dnwJw | null | [
"Treatment Response and Outcome/Disease Prediction",
"Image Reconstruction",
"Image Segmentation",
"Modalities - other"
] | [] | [] | null | null | Orthodontic treatment typically lasts for two years, and its outcome cannot be predicted intuitively in advance. In this paper, we propose a semantic-guided and knowledge-based generative framework to predict the visual outcome of orthodontic treatment from a single frontal photo. The framework involves four steps. Fir... | null | null |
Paper1008 | A Sheaf Theoretic Perspective for Robust Prostate Segmentation | [
"Ainkaran Santhirasekaram",
"Karen Pinto",
"Mathias Winkler",
"Andrea Rockall",
"Ben Glocker"
] | https://conferences.miccai.org/2023/papers/025-Paper1008.html | null | 10.1007/978-3-031-43901-8_24 | https://rdcu.be/dnwC8 | null | [
"Image Segmentation",
"Machine Learning - Model Generalizability / Federated Learning",
"Modalities - MRI"
] | [
"https://github.com/AinkaranSanthi/A-Sheaf-Theoretic-Perspective-for-Robust-Segmentation"
] | [
"http://medicaldecathlon.com/dataaws/",
"https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=21267207"
] | null | null | Deep learning based methods have become the most popular approach for prostate segmentation in MRI. However, domain variations due to the complex acquisition process result in textural differences as well as imaging artefacts which significantly affects the robustness of deep learning models for prostate segmentation a... | null | null |
Paper2327 | A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding | [
"Tianyang Zhong",
"Xiaozheng Wei",
"Enze Shi",
"Jiaxing Gao",
"Chong Ma",
"Yaonai Wei",
"Songyao Zhang",
"Lei Guo",
"Junwei Han",
"Tianming Liu",
"Tuo Zhang"
] | https://conferences.miccai.org/2023/papers/026-Paper2327.html | null | 10.1007/978-3-031-43907-0_40 | https://rdcu.be/dnwcR | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Machine Learning - Active Learning",
"Modalities - EEG/ECG"
] | [] | [] | null | null | Motor imagery (MI) electroencephalogram (EEG) decoding, as a core component widely used in noninvasive brain-computer interface (BCI) system, is critical to realize the interaction purpose of physical world and brain activity. However, the conventional methods are challenging to obtain desirable results for two main re... | null | null |
Paper1755 | A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos | [
"Chao Qin",
"Jiale Cao",
"Huazhu Fu",
"Rao Muhammad Anwer",
"Fahad Shahbaz Khan"
] | https://conferences.miccai.org/2023/papers/027-Paper1755.html | null | 10.1007/978-3-031-43895-0_45 | https://rdcu.be/dnwyY | null | [
"Machine Learning - Attention models",
"Clinical applications - Breast",
"Computer Aided Diagnosis",
"Modalities - Ultrasound",
"Modalities - Video"
] | [
"https://github.com/AlfredQin/STNet"
] | [] | null | null | Detecting breast lesion in videos is crucial for computer- aided diagnosis. Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of deep backbone fea- tures based on the self-attention operation. We argue that such a strat- egy struggles to effectively perform deep feat... | 2309.04702 | title_snapshot |
Paper2027 | A Spatial-Temporally Adaptive PINN Framework for 3D Bi-Ventricular Electrophysiological Simulations and Parameter Inference | [
"Yubo Ye",
"Huafeng Liu",
"Xiajun Jiang",
"Maryam Toloubidokhti",
"Linwei Wang"
] | https://conferences.miccai.org/2023/papers/028-Paper2027.html | null | 10.1007/978-3-031-43990-2_16 | https://rdcu.be/dnwLr | null | [
"Computational Anatomy and Physiology",
"Clinical applications - Cardiac",
"Interventional Simulation Systems",
"Machine Learning - Other",
"Modalities - EEG/ECG"
] | [] | [] | null | null | Physics-informed neural network (PINN) is a new paradigm for solving the forward and inverse problems of partial differential equations (PDEs). Its penetration into 3D bi-ventricular electrophysiology (EP) however has been slow, owing to its fundamental limitations to solve PDEs over large or complex solution domains w... | null | null |
Paper0501 | A Style Transfer-based Augmentation Framework for Improving Segmentation and Classification Performance across Different Sources in Ultrasound Images | [
"Bin Huang",
"Ziyue Xu",
"Shing-Chow Chan",
"Zhong Liu",
"Huiying Wen",
"Chao Hou",
"Qicai Huang",
"Meiqin Jiang",
"Changfeng Dong",
"Jie Zeng",
"Ruhai Zou",
"Bingsheng Huang",
"Xin Chen",
"Shuo Li"
] | https://conferences.miccai.org/2023/papers/029-Paper0501.html | null | 10.1007/978-3-031-43987-2_5 | https://rdcu.be/dnwJn | null | [
"Modalities - Ultrasound",
"Clinical applications - Abdomen",
"Computer Aided Diagnosis",
"Image Segmentation"
] | [] | [] | null | null | Ultrasound imaging can vary in style/appearance due to differences in scanning equipment and other factors, resulting in degraded segmentation and classification performance of deep learning models for ultrasound image analysis. Previous studies have attempted to solve this problem by using style transfer and augmentat... | null | null |
Paper3722 | A Texture Neural Network to Predict the Abnormal Brachial Plexus from Routine Magnetic Resonance Imaging | [
"Weiguo Cao",
"Benjamin M. Howe",
"Nicholas G. Rhodes",
"Sumana Ramanathan",
"Panagiotis Korfiatis",
"Kimberly K. Amrami",
"Robert J. Spinner",
"Timothy L. Kline"
] | https://conferences.miccai.org/2023/papers/030-Paper3722.html | null | 10.1007/978-3-031-43993-3_46 | https://rdcu.be/dnwNR | null | [
"Clinical applications - Neuroimaging - Others",
"Computer Aided Diagnosis",
"Machine Learning - Other",
"Modalities - MRI"
] | [] | [] | null | null | Brachial plexopathy is a form of peripheral neuropathy, which occurs when there is damage to the brachial plexus (BP). However, the diagnosis of breast cancer related BP from radiological imaging is still a great challenge. This paper proposes a texture pattern based convolutional neural network, called TPPNet, to carr... | null | null |
Paper1001 | A Transfer Learning Approach to Localise a Deep Brain Stimulation Target | [
"Ying-Qiu Zheng",
"Harith Akram",
"Stephen Smith",
"Saad Jbabdi"
] | https://conferences.miccai.org/2023/papers/031-Paper1001.html | null | 10.1007/978-3-031-43996-4_17 | https://rdcu.be/dnwOR | null | [
"Image-Guided Interventions and Surgery",
"Machine Learning - Transfer learning",
"Modalities - MRI",
"Surgical Data Science",
"Surgical Planning and Simulation"
] | [
"https://git.fmrib.ox.ac.uk/yqzheng1/hqaugmentation.jl",
"https://git.fmrib.ox.ac.uk/yqzheng1/python-localise"
] | [
"https://www.humanconnectome.org/study/hcp-young-adult/data-releases",
"https://www.ukbiobank.ac.uk/enable-your-research/about-our-data/imaging-data"
] | null | null | The ventral intermediate nucleus of thalamus (Vim) is a well-established surgical target in magnetic resonance-guided (MR-guided) surgery for the treatment of tremor. As the structure is not identifiable from conventional MR sequences, targeting the Vim has predominantly relied on standardised Vim atlases and thus fail... | null | null |
Paper1827 | A Unified Deep-Learning-Based Framework for Cochlear Implant Electrode Array Localization | [
"Yubo Fan",
"Jianing Wang",
"Yiyuan Zhao",
"Rui Li",
"Han Liu",
"Robert F. Labadie",
"Jack H. Noble",
"Benoit M. Dawant"
] | https://conferences.miccai.org/2023/papers/032-Paper1827.html | null | 10.1007/978-3-031-43996-4_36 | https://rdcu.be/dnwPf | null | [
"Image-Guided Interventions and Surgery",
"Machine Learning - Other",
"Modalities - CT",
"Visualization in Biomedical Imaging"
] | [] | [] | null | null | Cochlear implants (CIs) are neuroprosthetics that can provide a sense of sound to people with severe-to-profound hearing loss. A CI contains an electrode array (EA) that is threaded into the cochlea during surgery. Recent studies have shown that hearing outcomes are correlated with EA placement. An image-guided cochlea... | null | null |
Paper2320 | A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants | [
"Shaotong Zhu",
"Michael Wan",
"Elaheh Hatamimajoumerd",
"Kashish Jain",
"Samuel Zlota",
"Cholpady Vikram Kamath",
"Cassandra B. Rowan",
"Emma C. Grace",
"Matthew S. Goodwin",
"Marie J. Hayes",
"Rebecca A. Schwartz-Mette",
"Emily Zimmerman",
"Sarah Ostadabbas"
] | https://conferences.miccai.org/2023/papers/033-Paper2320.html | null | 10.1007/978-3-031-43895-0_55 | https://rdcu.be/dnwzn | null | [
"Machine Learning - Data Efficient Learning",
"Modalities - Video"
] | [
"https://github.com/ostadabbas/NNS-Detection-and-Segmentation"
] | [
"https://github.com/ostadabbas/NNS-Detection-and-Segmentation"
] | null | null | We present an end-to-end computer vision pipeline to detect non-nutritive sucking (NNS)—an infant sucking pattern with no nutrition delivered—as a potential biomarker for developmental delays, using off-the-shelf baby monitor video footage. One barrier to clinical (or algorithmic) assessment of NNS stems from its spars... | 2303.16867 | title_snapshot |
Paper2791 | A2FSeg: Adaptive Multi-Modal Fusion Network for Medical Image Segmentation | [
"Zirui Wang",
"Yi Hong"
] | https://conferences.miccai.org/2023/papers/034-Paper2791.html | null | 10.1007/978-3-031-43901-8_64 | https://rdcu.be/dnwEc | null | [
"Image Segmentation",
"Clinical applications - Oncology",
"Machine Learning - Attention models",
"Modalities - MRI"
] | [
"https://github.com/Zirui0623/A2FSeg.git"
] | [
"https://www.med.upenn.edu/cbica/brats2020/data.html"
] | null | null | Magnetic Resonance Imaging (MRI) plays an important role in multi-modal brain tumor segmentation. However, missing modality is very common in clinical diagnosis, which will lead to severe segmentation performance degradation. In this paper, we propose a simple adaptive multi-modal fusion network for brain tumor segment... | null | null |
Paper2655 | ACC-UNet: A Completely Convolutional UNet model for the 2020s | [
"Nabil Ibtehaz",
"Daisuke Kihara"
] | https://conferences.miccai.org/2023/papers/035-Paper2655.html | null | 10.1007/978-3-031-43898-1_66 | https://rdcu.be/dnwB0 | null | [
"Image Segmentation"
] | [
"https://github.com/kiharalab/ACC-UNet"
] | [
"https://challenge.isic-archive.com/data/#2018",
"https://polyp.grand-challenge.org/CVCClinicDB/",
"https://medicalsegmentation.com/covid19/",
"https://scholar.cu.edu.eg/?q=afahmy/pages/dataset",
"https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/"
] | null | null | This decade is marked by the introduction of Vision Transformer, a radical paradigm shift in broad computer vision. A similar trend is followed in medical imaging, UNet, one of the most influential architectures, has been redesigned with transformers. Recently, the efficacy of convolutional models in vision is being re... | 2308.13680 | title_snapshot |
Paper2373 | Accurate and Robust Patient Height and Weight Estimation in Clinical Imaging using a Depth Camera | [
"Birgi Tamersoy",
"Felix Alexandru Pîrvan",
"Santosh Pai",
"Ankur Kapoor"
] | https://conferences.miccai.org/2023/papers/036-Paper2373.html | null | 10.1007/978-3-031-43987-2_33 | https://rdcu.be/dnwJO | null | [
"Rigorous Evaluations of Methodology in Clinical Workflows",
"Machine Learning - Other",
"Machine Learning - Transfer learning",
"Modalities - CT",
"Modalities - MRI",
"Modalities - other"
] | [] | [] | null | null | Accurate and robust estimation of the patient’s height and weight is essential for many clinical imaging workflows. Patient’s safety, as well as a number of scan optimizations, rely on this information. In this paper we present a deep-learning based method for estimating the patient’s height and weight in unrestricted ... | null | null |
Paper1541 | Accurate multi-contrast MRI super-resolution via a dual cross-attention transformer network | [
"Shoujin Huang",
"Jingyu Li",
"Lifeng Mei",
"Tan Zhang",
"Ziran Chen",
"Yu Dong",
"Linzheng Dong",
"Shaojun Liu",
"Mengye Lyu"
] | https://conferences.miccai.org/2023/papers/037-Paper1541.html | null | 10.1007/978-3-031-43999-5_30 | https://rdcu.be/dnwwJ | null | [
"Image Reconstruction",
"Modalities - MRI"
] | [
"https://github.com/Solor-pikachu/DCAMSR"
] | [
"https://github.com/facebookresearch/fastMRI",
"https://github.com/mylyu/M4Raw"
] | null | null | Magnetic Resonance Imaging (MRI) is a critical imaging tool in clinical diagnosis, but obtaining high-resolution MRI images can be challenging due to hardware and scan time limitations. Recent studies have shown that using reference images from multi-contrast MRI data could improve super-resolution quality. However, th... | null | null |
Paper0867 | ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast | [
"Chenyu You",
"Weicheng Dai",
"Yifei Min",
"Lawrence Staib",
"Jas Sekhon",
"James S. Duncan"
] | https://conferences.miccai.org/2023/papers/038-Paper0867.html | null | 10.1007/978-3-031-43901-8_19 | https://rdcu.be/dnwC3 | null | [
"Image Segmentation",
"Machine Learning - Data Efficient Learning",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning"
] | [
"https://github.com/charlesyou999648/ACTION"
] | [] | null | null | Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes, i.e., boundary regions or rare objects. Recent work has significantly improved semi-supervised medical image segmentation in long-tailed scenarios by equipping them wi... | 2304.02689 | title_snapshot |
Paper1160 | ACT-Net: Anchor-context Action Detection in Surgery Videos | [
"Luoying Hao",
"Yan Hu",
"Wenjun Lin",
"Qun Wang",
"Heng Li",
"Huazhu Fu",
"Jinming Duan",
"Jiang Liu"
] | https://conferences.miccai.org/2023/papers/039-Paper1160.html | null | 10.1007/978-3-031-43996-4_19 | https://rdcu.be/dnwOT | null | [
"Surgical Scene Understanding",
"Modalities - Video",
"Surgical Skill and Work Flow Analysis"
] | [] | [] | null | null | Recognition and localization of surgical detailed actions is an essential component of developing a context-aware decision support system. However, most existing detection algorithms fail to provide high-accuracy action classes even having their locations, as they do not consider the surgery procedure’s regularity in t... | 2310.03377 | title_snapshot |
Paper2061 | Acute Ischemic Stroke Onset Time Classification with Dynamic Convolution and Perfusion Maps Fusion | [
"Peng Yang",
"Yuchen Zhang",
"Haijun Lei",
"Yueyan Bian",
"Qi Yang",
"Baiying Lei"
] | https://conferences.miccai.org/2023/papers/040-Paper2061.html | null | 10.1007/978-3-031-43904-9_54 | https://rdcu.be/dnwH0 | null | [
"Computer Aided Diagnosis",
"Clinical applications - Neuroimaging - Others"
] | [] | [] | null | null | In treating acute ischemic stroke (AIS), determining the time since stroke onset (TSS) is crucial. Computed tomography perfusion (CTP) is vital for determining TSS by providing sufficient cerebral blood flow information. However, the CTP has small samples and high dimensions. In addition, the CTP is multi-map data, whi... | null | null |
Paper1499 | Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases | [
"Wentao Zhang",
"Yujun Huang",
"Tong Zhang",
"Qingsong Zou",
"Wei-Shi Zheng",
"Ruixuan Wang"
] | https://conferences.miccai.org/2023/papers/041-Paper1499.html | null | 10.1007/978-3-031-43895-0_7 | https://rdcu.be/dnwxP | null | [
"Machine Learning - Continual Learning",
"Computer Aided Diagnosis",
"Machine Learning - Other"
] | [
"https://github.com/GiantJun/CL_Pytorch"
] | [
"https://challenge.isic-archive.com/data/#2019",
"https://medmnist.com/",
"https://www.cs.toronto.edu/~kriz/cifar.html",
"https://drive.google.com/drive/folders/1LMHgawD83Z5EmYN6wtLVIibZNrAZglZt?usp=sharing"
] | null | null | Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease ... | 2304.09042 | title_snapshot |
Paper2125 | Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation | [
"Muhammad Asad",
"Helena Williams",
"Indrajeet Mandal",
"Sarim Ather",
"Jan Deprest",
"Jan D'hooge",
"Tom Vercauteren"
] | https://conferences.miccai.org/2023/papers/042-Paper2125.html | null | 10.1007/978-3-031-43895-0_53 | https://rdcu.be/dnwzl | null | [
"Machine Learning - Data Efficient Learning",
"Modalities - CT"
] | [
"https://github.com/masadcv/MONet-MONAILabel"
] | [] | null | null | Existing interactive segmentation methods leverage automatic segmentation and user interactions for label refinement, significantly reducing the annotation workload compared to manual annotation. However, these methods lack quick adaptability to ambiguous and noisy data, which is a challenge in CT volumes containing lu... | 2303.13696 | title_snapshot |
Paper1681 | Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation | [
"Jingna Qiu",
"Frauke Wilm",
"Mathias Öttl",
"Maja Schlereth",
"Chang Liu",
"Tobias Heimann",
"Marc Aubreville",
"Katharina Breininger"
] | https://conferences.miccai.org/2023/papers/043-Paper1681.html | null | 10.1007/978-3-031-43895-0_9 | https://rdcu.be/dnwxR | null | [
"Machine Learning - Active Learning",
"Modalities - Histopathology"
] | [
"https://github.com/DeepMicroscopy/AdaptiveRegionSelection"
] | [
"http://gigadb.org/dataset/100439"
] | null | null | The process of annotating histological gigapixel-sized whole slide images (WSIs) at the pixel level for the purpose of training a supervised segmentation model is time-consuming. Region-based active learning (AL) involves training the model on a limited number of annotated image regions instead of requesting annotation... | 2307.07168 | title_snapshot |
Paper1894 | Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairs | [
"Fangda Li",
"Zhiqiang Hu",
"Wen Chen",
"Avinash Kak"
] | https://conferences.miccai.org/2023/papers/044-Paper1894.html | null | 10.1007/978-3-031-43987-2_61 | https://rdcu.be/dnwKh | null | [
"Computational (Integrative) Pathology",
"Clinical applications - Breast",
"Image Reconstruction",
"Machine Learning - Other",
"Modalities - Histopathology",
"Modalities - Microscopy"
] | [
"https://github.com/lifangda01/AdaptiveSupervisedPatchNCE"
] | [
"https://github.com/lifangda01/AdaptiveSupervisedPatchNCE"
] | null | null | Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more expensive in terms of both labor and the laboratory equipment required. This motivates recent research that demonstrates that the correlatio... | 2303.06193 | title_snapshot |
Paper0722 | Additional Positive Enables Better Representation Learning for Medical Images | [
"Dewen Zeng",
"Yawen Wu",
"Xinrong Hu",
"Xiaowei Xu",
"Jingtong Hu",
"Yiyu Shi"
] | https://conferences.miccai.org/2023/papers/045-Paper0722.html | null | 10.1007/978-3-031-43907-0_12 | https://rdcu.be/dnwca | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning"
] | [] | [
"https://challenge.isic-archive.com/landing/2019/",
"https://nihcc.app.box.com/v/ChestXray-NIHCC"
] | null | null | This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that in... | 2306.00112 | title_snapshot |
Paper1865 | Adjustable Robust Transformer for High Myopia Screening in Optical Coherence Tomography | [
"Xiao Ma",
"Zetian Zhang",
"Zexuan Ji",
"Kun Huang",
"Na Su",
"Songtao Yuan",
"Qiang Chen"
] | https://conferences.miccai.org/2023/papers/046-Paper1865.html | null | 10.1007/978-3-031-43904-9_49 | https://rdcu.be/dnwHu | null | [
"Computer Aided Diagnosis",
"Clinical applications - Ophthalmology",
"Machine Learning - Uncertainty",
"Modalities - other"
] | [
"https://github.com/maxiao0234/ARTran"
] | [] | null | null | Myopia is a manifestation of visual impairment caused by an excessively elongated eyeball. Image data is critical material for studying high myopia and pathological myopia. Measurements of spherical equivalent and axial length are the gold standards for identifying high myopia, but the available image data for matching... | 2312.07052 | title_snapshot |
Paper0340 | Adult-like Phase and Multi-scale Assistance for Isointense Infant Brain Tissue Segmentation | [
"Jiameng Liu",
"Feihong Liu",
"Kaicong Sun",
"Mianxin Liu",
"Yuhang Sun",
"Yuyan Ge",
"Dinggang Shen"
] | https://conferences.miccai.org/2023/papers/047-Paper0340.html | null | 10.1007/978-3-031-43901-8_6 | https://rdcu.be/dnwCG | null | [
"Image Segmentation",
"Modalities - MRI"
] | [
"https://github.com/SaberPRC/IsointenseBrainTissueSeg.git"
] | [
"https://www.nitrc.org/projects/ndarportal/"
] | null | null | Precise brain tissue segmentation is crucial for infant development tracking and early brain disorder diagnosis. However, it remains challenging to automatically segment the brain tissues of a 6-month-old infant (isointense phase), even for manual labeling, due to inherent ongoing myelination during the first postnatal... | null | null |
Paper1475 | AirwayFormer: Structure-Aware Boundary-Adaptive Transformers for Airway Anatomical Labeling | [
"Weihao Yu",
"Hao Zheng",
"Yun Gu",
"Fangfang Xie",
"Jiayuan Sun",
"Jie Yang"
] | https://conferences.miccai.org/2023/papers/048-Paper1475.html | null | 10.1007/978-3-031-43990-2_37 | https://rdcu.be/dnwLS | null | [
"Clinical applications - Lung",
"Machine Learning - Other"
] | [
"https://github.com/EndoluminalSurgicalVision-IMR/AirwayFormer"
] | [
"https://github.com/yuyouxixi/airway-labeling/tree/main"
] | null | null | Pulmonary airway labeling identifies anatomical names for branches in bronchial trees. These fine-grained labels are critical for disease diagnosis and intra-operative navigation. Recently, various methods have been proposed for this task. However, accurate labeling of each bronchus is challenging due to the fine-grain... | null | null |
Paper0690 | Alias-Free Co-Modulated Network for Cross-Modality Synthesis and Super-Resolution of MR Images | [
"Zhiyun Song",
"Xin Wang",
"Xiangyu Zhao",
"Sheng Wang",
"Zhenrong Shen",
"Zixu Zhuang",
"Mengjun Liu",
"Qian Wang",
"Lichi Zhang"
] | https://conferences.miccai.org/2023/papers/049-Paper0690.html | null | 10.1007/978-3-031-43999-5_7 | https://rdcu.be/dnwjh | null | [
"Image Reconstruction",
"Machine Learning - Other",
"Modalities - MRI"
] | [
"https://github.com/zhiyuns/AFCM"
] | [
"https://brain-development.org/ixi-dataset/",
"https://adni.loni.usc.edu/"
] | null | null | Cross-modality synthesis (CMS) and super-resolution (SR) have both been extensively studied with learning-based methods, which aim to synthesize desired modality images and reduce slice thickness for magnetic resonance imaging (MRI), respectively. It is also desirable to build a network for simultaneous cross-modality ... | 2311.08225 | title_judge |
Paper2820 | ALL-IN: A Local GLobal Graph-based DIstillatioN Model for Representation Learning of Gigapixel Histopathology Images With Application In Cancer Risk Assessment | [
"Puria Azadi",
"Jonathan Suderman",
"Ramin Nakhli",
"Katherine Rich",
"Maryam Asadi",
"Sonia Kung",
"Htoo Oo",
"Mira Keyes",
"Hossein Farahani",
"Calum MacAulay",
"Larry Goldenberg",
"Peter Black",
"Ali Bashashati"
] | https://conferences.miccai.org/2023/papers/050-Paper2820.html | null | 10.1007/978-3-031-43987-2_74 | https://rdcu.be/dnwKx | null | [
"Computational (Integrative) Pathology",
"Clinical applications - Oncology",
"Modalities - Histopathology",
"Treatment Response and Outcome/Disease Prediction"
] | [
"https://github.com/pazadimo/ALL-IN"
] | [] | null | null | The utility of machine learning models in histopathology image analysis for disease diagnosis has been extensively studied. However, efforts to stratify patient risk are relatively under-explored. While most current techniques utilize small fields of view (so-called local features) to link histopathology images to pati... | null | null |
Paper1205 | AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays | [
"Behzad Bozorgtabar",
"Dwarikanath Mahapatra",
"Jean-Philippe Thiran"
] | https://conferences.miccai.org/2023/papers/051-Paper1205.html | null | 10.1007/978-3-031-43907-0_19 | https://rdcu.be/dnwch | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Clinical applications - Lung",
"Computer Aided Diagnosis",
"Modalities - other"
] | [] | [
"https://www.kaggle.com/c/rsna-pneumonia-detection-challenge",
"https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection",
"https://nihcc.app.box.com/v/ChestXray-NIHCC/file/37164782321"
] | null | null | Unsupervised anomaly detection in medical images such as chest radiographs is stepping into the spotlight as it mitigates the scarcity of the labor-intensive and costly expert annotation of anomaly data. However, nearly all existing methods are formulated as a one-class classification trained only on representations fr... | 2307.12721 | title_snapshot |
Paper0989 | AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor | [
"Yu-Jen Chen",
"Xinrong Hu",
"Yiyu Shi",
"Tsung-Yi Ho"
] | https://conferences.miccai.org/2023/papers/052-Paper0989.html | null | 10.1007/978-3-031-43907-0_17 | https://rdcu.be/dnwcf | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Image Segmentation"
] | [
"https://github.com/windstormer/AME-CAM"
] | [] | null | null | Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning. To reduce the labor and expertise required for labeling, weakly-supervised semantic segmentation (WSSS) methods with class activation mapping (CAM) have been proposed. However... | 2306.14505 | title_snapshot |
Paper2596 | An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment | [
"Parmida Ghahremani",
"Joseph Marino",
"Juan Hernandez-Prera",
"Janis V. de la Iglesia",
"Robbert J. C. Slebos",
"Christine H. Chung",
"Saad Nadeem"
] | https://conferences.miccai.org/2023/papers/053-Paper2596.html | null | 10.1007/978-3-031-43987-2_68 | https://rdcu.be/dnwKr | null | [
"Computational (Integrative) Pathology",
"Clinical applications - Oncology",
"Modalities - Histopathology",
"Visualization in Biomedical Imaging"
] | [
"https://github.com/nadeemlab/DeepLIIF"
] | [
"https://github.com/nadeemlab/DeepLIIF"
] | null | null | We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with chea... | 2305.16465 | title_snapshot |
Paper1278 | An Anti-Biased TBSRTC-Category Aware Nuclei Segmentation Framework with A Multi-Label Thyroid Cytology Benchmark | [
"Junchao Zhu",
"Yiqing Shen",
"Haolin Zhang",
"Jing Ke"
] | https://conferences.miccai.org/2023/papers/054-Paper1278.html | null | 10.1007/978-3-031-43987-2_56 | https://rdcu.be/dnwKb | null | [
"Computational (Integrative) Pathology",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - Histopathology"
] | [
"https://github.com/Junchao-Zhu/TCSegNet"
] | [] | null | null | The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) has been widely accepted as a reliable criterion for thyroid cytology diagnosis, where extensive diagnostic information can be deduced from the allocation and boundary of cell nuclei. However, two major challenges hinder accurate nuclei segmentation from ... | null | null |
Paper2173 | An Auto-Encoder to Reconstruct Structure with Cryo-EM Images via Theoretically Guaranteed Isometric Latent Space, and its Application for Automatically Computing the Conformational Pathway | [
"Kimihiro Yamazaki",
"Yuichiro Wada",
"Atsushi Tokuhisa",
"Mutsuyo Wada",
"Takashi Katoh",
"Yuhei Umeda",
"Yasushi Okuno",
"Akira Nakagawa"
] | https://conferences.miccai.org/2023/papers/055-Paper2173.html | null | 10.1007/978-3-031-43907-0_38 | https://rdcu.be/dnwcP | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Image Reconstruction",
"Visualization in Biomedical Imaging"
] | [] | [] | null | null | Structural analysis by cryo-electron microscopy (Cryo-EM) has become well-established in the field of structural biology. Recently, cutting-edge methods have been proposed for the purpose of reconstructing either a small set of structures or a conformational pathway (continuous structural change), where a 3D density ma... | null | null |
Paper2970 | An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field | [
"Kelly Payette",
"Alena Uus",
"Jordina Aviles Verdera",
"Carla Avena Zampieri",
"Megan Hall",
"Lisa Story",
"Maria Deprez",
"Mary A. Rutherford",
"Joseph V. Hajnal",
"Sébastien Ourselin",
"Raphael Tomi-Tricot",
"Jana Hutter"
] | https://conferences.miccai.org/2023/papers/056-Paper2970.html | null | 10.1007/978-3-031-43990-2_34 | https://rdcu.be/dnwLP | null | [
"Clinical applications - Fetal Imaging",
"Image Reconstruction"
] | [
"https://github.com/SVRTK/Fetal-T2star-Recon"
] | [] | null | null | Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese p... | 2308.04903 | title_snapshot |
Paper0599 | An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis | [
"Luyi Han",
"Tianyu Zhang",
"Yunzhi Huang",
"Haoran Dou",
"Xin Wang",
"Yuan Gao",
"Chunyao Lu",
"Tao Tan",
"Ritse Mann"
] | https://conferences.miccai.org/2023/papers/057-Paper0599.html | null | 10.1007/978-3-031-43999-5_5 | https://rdcu.be/dnwjf | null | [
"Image Reconstruction",
"Clinical applications - Neuroimaging - Brain Development",
"Machine Learning - Interpretability / Explainability",
"Modalities - MRI"
] | [
"https://github.com/fiy2W/mri_seq2seq"
] | [
"http://braintumorsegmentation.org/"
] | null | null | Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-based methods have achieved good performance in combining multiple ava... | 2307.00885 | title_snapshot |
Paper3214 | An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment | [
"Favour Nerrise",
"Qingyu Zhao",
"Kathleen L. Poston",
"Kilian M. Pohl",
"Ehsan Adeli"
] | https://conferences.miccai.org/2023/papers/058-Paper3214.html | null | 10.1007/978-3-031-43895-0_68 | https://rdcu.be/dnwzA | null | [
"Machine Learning - Attention models",
"Clinical applications - Neuroimaging - Functional Brain Networks",
"Computer Aided Diagnosis",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - MRI"
] | [
"https://github.com/favour-nerrise/xGW-GAT"
] | [] | null | null | One of the hallmark symptoms of Parkinson’s Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing... | 2307.13108 | title_snapshot |
Paper3355 | An Interpretable and Attention-based Method for Gaze Estimation Using Electroencephalography | [
"Nina Weng",
"Martyna Plomecka",
"Manuel Kaufmann",
"Ard Kastrati",
"Roger Wattenhofer",
"Nicolas Langer"
] | https://conferences.miccai.org/2023/papers/059-Paper3355.html | null | 10.1007/978-3-031-43895-0_69 | https://rdcu.be/dnwzB | null | [
"Modalities - EEG/ECG",
"Machine Learning - Attention models"
] | [] | [] | null | null | Eye movements can reveal valuable insights into various aspects of human mental processes, physical well-being, and actions. Recently, several datasets have been made available that simultaneously record EEG activity and eye movements. This has triggered the development of various methods to predict gaze direction base... | 2308.05768 | title_snapshot |
Paper1858 | An Unsupervised Multispectral Image Registration Network for Skin Diseases | [
"Songhui Diao",
"Wenxue Zhou",
"Chenchen Qin",
"Jun Liao",
"Junzhou Huang",
"Wenming Yang",
"Jianhua Yao"
] | https://conferences.miccai.org/2023/papers/060-Paper1858.html | null | 10.1007/978-3-031-43999-5_68 | https://rdcu.be/dnwxl | null | [
"Image Registration",
"Clinical applications - Dermatology",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning"
] | [
"https://github.com/SH-Diao123/MSIR"
] | [] | null | null | Multispectral imaging has a broad, promising and advantageous application prospect in the diagnosis of skin diseases. However, there are inherent deviations such as rigid or non-rigid deformation among multispectral images (MSI), which makes accurate and robust registration algorithms desirable to extract reliable mult... | null | null |
Paper2566 | Analysis of Suture Force Simulations for Optimal Orientation of Rhomboid Skin Flaps | [
"Wenzhangzhi Guo",
"Ty Trusty",
"Joel C. Davies",
"Vito Forte",
"Eitan Grinspun",
"Lueder A. Kahrs"
] | https://conferences.miccai.org/2023/papers/061-Paper2566.html | null | 10.1007/978-3-031-43996-4_55 | https://rdcu.be/dnwPZ | null | [
"Surgical Planning and Simulation",
"Clinical applications - Dermatology",
"Image-Guided Interventions and Surgery",
"Modalities - Video",
"Surgical Visualization and Mixed/Augmented/Virtual Reality",
"Visualization in Biomedical Imaging"
] | [] | [] | null | null | Skin flap is a common technique used by surgeons to close the wound after the resection of a lesion. Careful planning of a skin flap procedure is essential for the most optimal functional and aesthetic outcome. However, currently surgical planning is mostly done based on surgeons’ experience and preferences. In this pa... | null | null |
Paper2830 | Anatomical Landmark Detection Using a Multiresolution Learning Approach with a Hybrid Transformer-CNN Model | [
"Thanaporn Viriyasaranon",
"Serie Ma",
"Jang-Hwan Choi"
] | https://conferences.miccai.org/2023/papers/062-Paper2830.html | null | 10.1007/978-3-031-43987-2_42 | https://rdcu.be/dnwJX | null | [
"Computer Aided Diagnosis",
"Machine Learning - Attention models",
"Modalities - CT",
"Visualization in Biomedical Imaging"
] | [
"https://github.com/seriee/Multiresolution-HTC.git"
] | [] | null | null | Accurate localization of anatomical landmarks has a critical role in clinical diagnosis, treatment planning, and research. Most existing deep learning methods for anatomical landmark localization rely on heatmap regression-based learning, which generates label representations as 2D Gaussian distributions centered at th... | null | null |
Paper0270 | Anatomical-aware Point-Voxel Network for Couinaud Segmentation in Liver CT | [
"Xukun Zhang",
"Yang Liu",
"Sharib Ali",
"Xiao Zhao",
"Mingyang Sun",
"Minghao Han",
"Tao Liu",
"Peng Zhai",
"Zhiming Cui",
"Peixuan Zhang",
"Xiaoying Wang",
"Lihua Zhang"
] | https://conferences.miccai.org/2023/papers/063-Paper0270.html | null | 10.1007/978-3-031-43898-1_45 | https://rdcu.be/dnwBF | null | [
"Image Segmentation",
"Clinical applications - Abdomen",
"Modalities - CT"
] | [] | [] | null | null | Accurately segmenting the liver into anatomical segments is crucial for surgical planning and lesion monitoring in CT imaging. However, this is a challenging task as it is defined based on vessel structures, and there is no intensity contrast between adjacent segments in CT images. In this paper, we propose a novel poi... | null | null |
Paper0383 | Anatomy-Driven Pathology Detection on Chest X-rays | [
"Philip Müller",
"Felix Meissen",
"Johannes Brandt",
"Georgios Kaissis",
"Daniel Rueckert"
] | https://conferences.miccai.org/2023/papers/064-Paper0383.html | null | 10.1007/978-3-031-43907-0_6 | https://rdcu.be/dnwb4 | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Computational Anatomy and Physiology",
"Computer Aided Diagnosis",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Transfer learning"
] | [
"https://github.com/philip-mueller/adpd"
] | [] | null | null | Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions. However, annotating pathology bounding boxes is a time-consuming task such that large public datasets for th... | 2309.02578 | title_snapshot |
Paper1594 | Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection | [
"Balint Kovacs",
"Nils Netzer",
"Michael Baumgartner",
"Carolin Eith",
"Dimitrios Bounias",
"Clara Meinzer",
"Paul F. Jäger",
"Kevin S. Zhang",
"Ralf Floca",
"Adrian Schrader",
"Fabian Isensee",
"Regula Gnirs",
"Magdalena Görtz",
"Viktoria Schütz",
"Albrecht Stenzinger",
"Markus Hohenf... | https://conferences.miccai.org/2023/papers/065-Paper1594.html | null | 10.1007/978-3-031-43990-2_50 | https://rdcu.be/dnwL5 | null | [
"Clinical applications - Oncology",
"Computational Anatomy and Physiology",
"Computer Aided Diagnosis",
"Machine Learning - Other",
"Modalities - MRI"
] | [
"https://github.com/MIC-DKFZ/anatomy_informed_DA",
"https://github.com/MIC-DKFZ/batchgenerators"
] | [] | null | null | Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis systems still rely on simplistic spatial transformations to preserve the pathological label post transformation. However, such augmentation... | 2309.03652 | title_snapshot |
Paper2034 | Aneurysm Pose Estimation with Deep Learning | [
"Youssef Assis",
"Liang Liao",
"Fabien Pierre",
"René Anxionnat",
"Erwan Kerrien"
] | https://conferences.miccai.org/2023/papers/066-Paper2034.html | null | 10.1007/978-3-031-43895-0_51 | https://rdcu.be/dnwzj | null | [
"Machine Learning - Data Efficient Learning",
"Clinical applications - Neuroimaging - Others",
"Clinical applications - Vascular",
"Computer Aided Diagnosis",
"Modalities - MRI"
] | [
"https://gitlab.inria.fr/yassis/DeepAnePose"
] | [
"https://openneuro.org/datasets/ds003949/versions/1.0.1"
] | null | null | The diagnosis of unruptured intracranial aneurysms from Magnetic Resonance Angiography (MRA) images is a challenging clinical problem that is extremely difficult to automate. We propose to go beyond the mere detection of each aneurysm and also estimate its size and the orientation of its main axis for an immediate visu... | null | null |
Paper0522 | AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography | [
"Ruisheng Su",
"Matthijs van der Sluijs",
"Sandra Cornelissen",
"Wim van Zwam",
"Aad van der Lugt",
"Wiro Niessen",
"Danny Ruijters",
"Theo van Walsum",
"Adrian Dalca"
] | https://conferences.miccai.org/2023/papers/067-Paper0522.html | null | 10.1007/978-3-031-43990-2_72 | https://rdcu.be/dnwMw | null | [
"Clinical applications - Vascular",
"Image Registration",
"Image Segmentation",
"Image-Guided Interventions and Surgery",
"Visualization in Biomedical Imaging"
] | [
"https://github.com/RuishengSu/AngioMoCo"
] | [] | null | null | Cerebral X-ray digital subtraction angiography (DSA) is the standard imaging technique for visualizing blood flow and guiding endovascular treatments. The quality of DSA is often negatively impacted by body motion during acquisition, leading to decreased diagnostic value. Traditional methods address motion correction b... | 2310.05445 | title_snapshot |
Paper2152 | Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models | [
"Tomer Amit",
"Shmuel Shichrur",
"Tal Shaharabany",
"Lior Wolf"
] | https://conferences.miccai.org/2023/papers/068-Paper2152.html | null | 10.1007/978-3-031-43901-8_52 | https://rdcu.be/dnwD0 | null | [
"Image Segmentation"
] | [
"https://github.com/tomeramit/Annotator-Consensus-Prediction"
] | [] | null | null | A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion models. Our method leverages the diffusion-based approach to incorporat... | 2306.09004 | title_snapshot |
Paper2200 | Anti-Adversarial Consistency Regularization for Data Augmentation: Applications to Robust Medical Image Segmentation | [
"Hyuna Cho",
"Yubin Han",
"Won Hwa Kim"
] | https://conferences.miccai.org/2023/papers/069-Paper2200.html | null | 10.1007/978-3-031-43901-8_53 | https://rdcu.be/dnwD1 | null | [
"Image Segmentation",
"Machine Learning - Active Learning",
"Machine Learning - Data Efficient Learning"
] | [] | [] | null | null | Modern deep learning methods for semantic segmentation require labor-intensive labeling for large-scale datasets with dense pixel-level annotations. Recent data augmentation methods such as dropping, mixing image patches, and adding random noises suggest effective ways to address the labeling issues for natural images.... | null | null |
Paper2382 | AR2T: Advanced Realistic Rendering Technique for Biomedical Volumes | [
"Elena Denisova",
"Leonardo Manetti",
"Leonardo Bocchi",
"Ernesto Iadanza"
] | https://conferences.miccai.org/2023/papers/070-Paper2382.html | null | 10.1007/978-3-031-43987-2_34 | https://rdcu.be/dnwJP | null | [
"Visualization in Biomedical Imaging",
"Image Reconstruction",
"Image-Guided Interventions and Surgery",
"Interventional Imaging Systems",
"Modalities - CT",
"Surgical Planning and Simulation"
] | [] | [
"https://www.kaggle.com/datasets/imaginar2t/cbctdata"
] | null | null | Three-dimensional (3D) rendering of biomedical volumes can be used to illustrate the diagnosis to patients, train inexperienced clinicians, or facilitate surgery planning for experts. The most realistic visualization can be achieved by the Monte-Carlo path tracing (MCPT) rendering technique which is based on the physic... | null | null |
Paper3171 | Ariadne's Thread: Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images | [
"Yi Zhong",
"Mengqiu Xu",
"Kongming Liang",
"Kaixin Chen",
"Ming Wu"
] | https://conferences.miccai.org/2023/papers/071-Paper3171.html | null | 10.1007/978-3-031-43901-8_69 | https://rdcu.be/dnwEm | null | [
"Image Segmentation",
"Clinical applications - Lung",
"Machine Learning - Other",
"Modalities - Text (clinical/radiology reports)"
] | [
"https://github.com/Junelin2333/LanGuideMedSeg-MICCAI2023"
] | [
"https://github.com/HUANGLIZI/LViT"
] | null | null | Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amou... | 2307.03942 | title_snapshot |
Paper1249 | ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models | [
"Yuhao Du",
"Yuncheng Jiang",
"Shuangyi Tan",
"Xusheng Wu",
"Qi Dou",
"Zhen Li",
"Guanbin Li",
"Xiang Wan"
] | https://conferences.miccai.org/2023/papers/072-Paper1249.html | null | 10.1007/978-3-031-43895-0_32 | https://rdcu.be/dnwyL | null | [
"Machine Learning - Other",
"Image Segmentation",
"Modalities - other"
] | [
"https://github.com/DuYooho/ArSDM"
] | [] | null | null | Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment. However, as medical image annotation is labour- and resource-intensive, the scarcity of annotated data limits the effectiveness and generalization of existing methods. Although rec... | 2309.01111 | title_snapshot |
Paper0943 | Artifact Restoration in Histology Images with Diffusion Probabilistic Models | [
"Zhenqi He",
"Junjun He",
"Jin Ye",
"Yiqing Shen"
] | https://conferences.miccai.org/2023/papers/073-Paper0943.html | null | 10.1007/978-3-031-43987-2_50 | https://rdcu.be/dnwJ5 | null | [
"Modalities - Histopathology"
] | [
"https://github.com/zhenqi-he/ArtiFusion"
] | [
"https://camelyon17.grand-challenge.org",
"https://github.com/lu-yizhou/ClusterSeg"
] | null | null | Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both pathologists and Computer-Aided Diagnosis (CAD) systems. Existing approaches to restoring artifact images are confined to Generative Adversarial Net... | 2307.14262 | title_snapshot |
Paper1934 | ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation | [
"Zihang Xu",
"Haifan Gong",
"Xiang Wan",
"Haofeng Li"
] | https://conferences.miccai.org/2023/papers/074-Paper1934.html | null | 10.1007/978-3-031-43990-2_31 | https://rdcu.be/dnwLM | null | [
"Computer Aided Diagnosis",
"Clinical applications - Fetal Imaging",
"Modalities - MRI"
] | [] | [] | null | null | Automatic tissue segmentation of fetal brain images is essential for the quantitative analysis of prenatal neurodevelopment. However, producing voxel-level annotations of fetal brain imaging is time-consuming and expensive. To reduce labeling costs, we propose a practical unsupervised domain adaptation (UDA) setting th... | 2310.14172 | title_snapshot |
Paper1927 | ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising | [
"Zhihao Chen",
"Qi Gao",
"Yi Zhang",
"Hongming Shan"
] | https://conferences.miccai.org/2023/papers/075-Paper1927.html | null | 10.1007/978-3-031-43999-5_34 | https://rdcu.be/dnwwN | null | [
"Image Reconstruction",
"Machine Learning - Attention models",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Other",
"Modalities - CT"
] | [
"https://github.com/hao1635/ASCON"
] | [] | null | null | While various deep learning methods have been proposed for low-dose computed tomography (CT) denoising, most of them leverage the normal-dose CT images as the ground-truth to supervise the denoising process. These methods typically ignore the inherent correlation within a single CT image, especially the anatomical sema... | 2307.12225 | title_snapshot |
Paper2274 | Assignment Theory-Augmented Neural Network for Dental Arch Labeling | [
"Tudor Dascalu",
"Bulat Ibragimov"
] | https://conferences.miccai.org/2023/papers/076-Paper2274.html | null | 10.1007/978-3-031-43898-1_29 | https://rdcu.be/dnwA1 | null | [
"Machine Learning - Other",
"Image Segmentation",
"Modalities - other"
] | [] | [] | null | null | Identifying and detecting a set of objects that conform to a structured pattern, but may also have misaligned, missing, or duplicated elements is a difficult task. Dental structures serve as a real-world example of such objects, with high variability in their shape, alignment, and number across different individuals. T... | null | null |
Paper1850 | Asymmetric Contour Uncertainty Estimation for Medical Image Segmentation | [
"Thierry Judge",
"Olivier Bernard",
"Woo-Jin Cho Kim",
"Alberto Gomez",
"Agisilaos Chartsias",
"Pierre-Marc Jodoin"
] | https://conferences.miccai.org/2023/papers/077-Paper1850.html | null | 10.1007/978-3-031-43898-1_21 | https://rdcu.be/dnwAS | null | [
"Machine Learning - Uncertainty",
"Clinical applications - Cardiac",
"Clinical applications - Lung",
"Image Segmentation",
"Modalities - CT",
"Modalities - Ultrasound"
] | [
"https://github.com/ThierryJudge/contouring-uncertainty"
] | [
"https://www.creatis.insa-lyon.fr/Challenge/camus/",
"http://db.jsrt.or.jp/eng.php"
] | null | null | Aleatoric uncertainty estimation is a critical step in medical image segmentation. Most techniques for estimating aleatoric uncertainty for segmentation purposes assume a Gaussian distribution over the neural network’s logit value modeling the uncertainty in the predicted class. However, in many cases, such as image se... | null | null |
Paper2614 | Attentive Deep Canonical Correlation Analysis for Diagnosing Alzheimer's Disease using Multimodal Imaging Genetics | [
"Rong Zhou",
"Houliang Zhou",
"Brian Y. Chen",
"Li Shen",
"Yu Zhang",
"Lifang He"
] | https://conferences.miccai.org/2023/papers/078-Paper2614.html | null | 10.1007/978-3-031-43895-0_64 | https://rdcu.be/dnwzw | null | [
"Machine Learning - Attention models",
"Integration of Imaging with Non-Imaging Biomarkers",
"Machine Learning - Interpretability / Explainability",
"Modalities - MRI",
"Modalities - PET/SPECT",
"Treatment Response and Outcome/Disease Prediction"
] | [
"https://github.com/rongzhou7/ADCCA"
] | [] | null | null | Integration of imaging genetics data provides unprecedented opportunities for revealing biological mechanisms underpinning diseases and certain phenotypes. In this paper, a new model called attentive deep canonical correlation analysis (ADCCA) is proposed for the diagnosis of Alzheimer’s disease using multimodal brain ... | null | null |
Paper1623 | atTRACTive: Semi-automatic white matter tract segmentation using active learning | [
"Robin Peretzke",
"Klaus H. Maier-Hein",
"Jonas Bohn",
"Yannick Kirchhoff",
"Saikat Roy",
"Sabrina Oberli-Palma",
"Daniela Becker",
"Pavlina Lenga",
"Peter Neher"
] | https://conferences.miccai.org/2023/papers/079-Paper1623.html | null | 10.1007/978-3-031-43993-3_23 | https://rdcu.be/dnwNo | null | [
"Clinical applications - Neuroimaging - DWI and Tractography",
"Machine Learning - Active Learning",
"Modalities - MRI"
] | [
"https://github.com/MIC-DKFZ/atTRACTive_simulations",
"https://github.com/MIC-DKFZ/MITK-Diffusion"
] | [
"https://zenodo.org/record/1477956"
] | null | null | Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatically. However, these models are primarily trained on healthy subjects an... | 2305.18905 | title_snapshot |
Paper1042 | AUA-dE: An adaptive uncertainty guided attention for diffusion MRI models estimation | [
"Tianshu Zheng",
"Ruicheng Ba",
"Xiaoli Wang",
"Chuyang Ye",
"Dan Wu"
] | https://conferences.miccai.org/2023/papers/080-Paper1042.html | null | 10.1007/978-3-031-43993-3_14 | https://rdcu.be/dnwNf | null | [
"Modalities - MRI",
"Clinical applications - Neuroimaging - DWI and Tractography",
"Machine Learning - Other"
] | [] | [] | null | null | Diffusion MRI (dMRI) is a well-established tool for probing tissue microstruc-ture properties. However, advanced dMRI models commonly have multiple compartments that are highly nonlinear and complex, and also require dense sampling in q-space. These problems have been investigated using deep learning based techniques. ... | null | null |
Paper3582 | Automated CT Lung Cancer Screening Workflow using 3D Camera | [
"Brian Teixeira",
"Vivek Singh",
"Birgi Tamersoy",
"Andreas Prokein",
"Ankur Kapoor"
] | https://conferences.miccai.org/2023/papers/081-Paper3582.html | null | 10.1007/978-3-031-43990-2_40 | https://rdcu.be/dnwLV | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Clinical applications - Lung",
"Modalities - CT"
] | [] | [] | null | null | Despite recent developments in CT planning that enabled automation in patient positioning, time-consuming scout scans are still needed to compute dose profile and ensure the patient is properly positioned. In this paper, we present a novel method which eliminates the need for scout scans in CT lung cancer screening by ... | 2309.15750 | title_snapshot |
Paper0174 | Automatic Bleeding Risk Rating System of Gastric Varices | [
"Yicheng Jiang",
"Luyue Shi",
"Wei Qi",
"Lei Chen",
"Guanbin Li",
"Xiaoguang Han",
"Xiang Wan",
"Siqi Liu"
] | https://conferences.miccai.org/2023/papers/082-Paper0174.html | null | 10.1007/978-3-031-43904-9_1 | https://rdcu.be/dnwGD | null | [
"Computer Aided Diagnosis",
"Treatment Response and Outcome/Disease Prediction"
] | [
"https://github.com/LuyueShi/gastric-varices"
] | [] | null | null | An automated bleeding risk rating system of gastric varices (GV) aims to predict the bleeding risk and severity of GV, in order to assist endoscopists in diagnosis and decrease the mortality rate of patients with liver cirrhosis and portal hypertension. However, since the lack of commonly accepted quantification standa... | null | null |
Paper0836 | Automatic Retrieval of Corresponding US Views in Longitudinal Examinations | [
"Hamideh Kerdegari",
"Nhat Phung Tran Huy",
"Van Hao Nguyen",
"Thi Phuong Thao Truong",
"Ngoc Minh Thu Le",
"Thanh Phuong Le",
"Thi Mai Thao Le",
"Luigi Pisani",
"Linda Denehy",
"Vital Consortium",
"Reza Razavi",
"Louise Thwaites",
"Sophie Yacoub",
"Andrew P. King",
"Alberto Gomez"
] | https://conferences.miccai.org/2023/papers/083-Paper0836.html | null | 10.1007/978-3-031-43907-0_15 | https://rdcu.be/dnwcd | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Image Segmentation",
"Machine Learning - Other",
"Modalities - Ultrasound"
] | [
"https://github.com/hamidehkerdegari/Muscle-view-retrieval"
] | [] | null | null | Skeletal muscle atrophy is a common occurrence in critically ill patients in the intensive care unit (ICU) who spend long periods in bed. Muscle mass must be recovered through physiotherapy before patient discharge and ultrasound imaging is frequently used to assess the recovery process by measuring the muscle size ove... | 2306.04739 | title_snapshot |
Paper2746 | Automatic Segmentation of Internal Tooth Structure from CBCT Images using Hierarchical Deep Learning | [
"SaeHyun Kim",
"In-Seok Song",
"Seung Jun Baek"
] | https://conferences.miccai.org/2023/papers/084-Paper2746.html | null | 10.1007/978-3-031-43898-1_67 | https://rdcu.be/dnwB1 | null | [
"Image Segmentation",
"Machine Learning - Other",
"Modalities - CT"
] | [
"https://github.com/Saeeeae/Internal-Tooth-Segmentation"
] | [] | null | null | Accurate segmentation of teeth is crucial for effective treatment planning. Previous approaches attempted to segment a tooth as a whole, which has limitations because most treatments involve internal structures of teeth. In this paper, we propose fully automated segmentation of internal tooth structure, including ename... | null | null |
Paper2247 | Automatic Surgical Reconstruction for Orbital Blow-out Fracture via Symmetric Prior Anatomical Knowledge-Guided Adversarial Generative Network | [
"Jiangchang Xu",
"Yining Wei",
"Huifang Zhou",
"Yinwei Li",
"Xiaojun Chen"
] | https://conferences.miccai.org/2023/papers/085-Paper2247.html | null | 10.1007/978-3-031-43996-4_44 | https://rdcu.be/dnwPp | null | [
"Surgical Planning and Simulation",
"Clinical applications - Ophthalmology"
] | [] | [] | null | null | Orbital blow-out fracture (OBF) is a complex disease that can cause severe dam-age to the orbital wall. The ultimate means of treating this disease is orbital wall repair surgery, where automatic reconstruction of the orbital wall is a crucial step. However, accurately reconstructing the orbital wall is a great challen... | null | null |
Paper0682 | B-Cos Aligned Transformers Learn Human-Interpretable Features | [
"Manuel Tran",
"Amal Lahiani",
"Yashin Dicente Cid",
"Melanie Boxberg",
"Peter Lienemann",
"Christian Matek",
"Sophia J. Wagner",
"Fabian J. Theis",
"Eldad Klaiman",
"Tingying Peng"
] | https://conferences.miccai.org/2023/papers/086-Paper0682.html | null | 10.1007/978-3-031-43993-3_50 | https://rdcu.be/dnwNV | null | [
"Computational (Integrative) Pathology",
"Machine Learning - Interpretability / Explainability",
"Modalities - Histopathology",
"Modalities - Microscopy"
] | [] | [
"https://zenodo.org/record/1214456",
"https://doi.org/10.7937/tcia.2019.36f5o9ld",
"https://portal.gdc.cancer.gov/projects/TCGA-COAD"
] | null | null | Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology. However, domain experts are still reluctant to use these models due to their lack of interpretability. This is not surprising, as critical decisions need to be transparent and understandable. The most comm... | 2401.08868 | title_snapshot |
Paper2016 | BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation | [
"Tao Chen",
"Chenhui Wang",
"Hongming Shan"
] | https://conferences.miccai.org/2023/papers/087-Paper2016.html | null | 10.1007/978-3-031-43901-8_47 | https://rdcu.be/dnwDV | null | [
"Image Segmentation",
"Clinical applications - Lung",
"Clinical applications - Neuroimaging - Others",
"Clinical applications - Oncology",
"Machine Learning - Uncertainty",
"Modalities - CT",
"Modalities - MRI"
] | [
"https://github.com/takimailto/BerDiff"
] | [] | null | null | Medical image segmentation is a challenging task with inherent ambiguity and high uncertainty attributed to factors such as unclear tumor boundaries and multiple plausible annotations. The accuracy and diversity of segmentation masks are both crucial for providing valuable references to radiologists in clinical practic... | 2304.04429 | title_snapshot |
Paper1399 | Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding | [
"Zijian Dong",
"Yilei Wu",
"Yu Xiao",
"Joanna Su Xian Chong",
"Yueming Jin",
"Juan Helen Zhou"
] | https://conferences.miccai.org/2023/papers/088-Paper1399.html | null | 10.1007/978-3-031-43904-9_34 | https://rdcu.be/dnwHd | null | [
"Computer Aided Diagnosis",
"Clinical applications - Neuroimaging - Functional Brain Networks",
"Machine Learning - Attention models",
"Modalities - MRI"
] | [
"https://github.com/ZijianD/Brain-TokenGT.git"
] | [
"https://adni.loni.usc.edu/",
"https://www.oasis-brains.org/"
] | null | null | Under the framework of network-based neurodegeneration, brain functional connectome (FC)-based Graph Neural Networks (GNN) have emerged as a valuable tool for the diagnosis and prognosis of neurodegenerative diseases such as Alzheimer’s disease (AD). However, these models are tailored for brain FC at a single time poin... | 2307.00858 | title_snapshot |
Paper1392 | Bidirectional Mapping with Contrastive Learning on Multimodal Neuroimaging Data | [
"Kai Ye",
"Haoteng Tang",
"Siyuan Dai",
"Lei Guo",
"Johnny Yuehan Liu",
"Yalin Wang",
"Alex Leow",
"Paul M. Thompson",
"Heng Huang",
"Liang Zhan"
] | https://conferences.miccai.org/2023/papers/089-Paper1392.html | null | 10.1007/978-3-031-43898-1_14 | https://rdcu.be/dnwAL | null | [
"Machine Learning - Other",
"Clinical applications - Neuroimaging - Functional Brain Networks",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - MRI",
"Treatment Response and Outcome/Disease Prediction"
] | [
"https://github.com/FlynnYe/BMCL"
] | [] | null | null | The modeling of the interaction between brain structure and function using deep learning techniques has yielded remarkable success in identifying potential biomarkers for different clinical phenotypes and brain diseases. However, most existing studies focus on one-way mapping, either projecting brain function to brain ... | null | null |
Paper2993 | BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior | [
"Yu Liu",
"Gesine Müller",
"Nassir Navab",
"Carsten Marr",
"Jan Huisken",
"Tingying Peng"
] | https://conferences.miccai.org/2023/papers/090-Paper2993.html | null | 10.1007/978-3-031-43993-3_62 | https://rdcu.be/dnwN7 | null | [
"Modalities - Microscopy",
"Image Reconstruction"
] | [] | [] | null | null | Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples with minimal photo-damage, experiences “defocused” image quality caused by light scattering when photons propagate through thick tissues. To circumvent this issue, dual-view imaging is particularl... | 2309.01865 | title_snapshot |
Paper1973 | Black-box Domain Adaptative Cell Segmentation via Multi-source Distillation | [
"Xingguang Wang",
"Zhongyu Li",
"Xiangde Luo",
"Jing Wan",
"Jianwei Zhu",
"Ziqi Yang",
"Meng Yang",
"Cunbao Xu"
] | https://conferences.miccai.org/2023/papers/091-Paper1973.html | null | 10.1007/978-3-031-43907-0_71 | https://rdcu.be/dnwdS | null | [
"Machine Learning - Transfer learning",
"Computational (Integrative) Pathology",
"Image Segmentation",
"Machine Learning - Model Generalizability / Federated Learning",
"Modalities - Histopathology"
] | [] | [] | null | null | Cell segmentation plays a critical role in diagnosing various cancers. Although deep learning techniques have been widely investigated, the enormous types and diverse appearances of histopathological cells still pose significant challenges for clinical applications. Moreover, data protection policies in different clini... | null | null |
Paper1650 | Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers | [
"Anlan Sun",
"Zhao Zhang",
"Meng Lei",
"Yuting Dai",
"Dong Wang",
"Liwei Wang"
] | https://conferences.miccai.org/2023/papers/092-Paper1650.html | null | 10.1007/978-3-031-43904-9_43 | https://rdcu.be/dnwHo | null | [
"Computer Aided Diagnosis",
"Clinical applications - Breast",
"Machine Learning - Attention models",
"Machine Learning - Interpretability / Explainability",
"Modalities - Ultrasound",
"Modalities - Video"
] | [
"https://github.com/PlayerSAL/KGA-Net"
] | [] | null | null | Breast ultrasound videos contain richer information than ultrasound images, therefore it is more meaningful to develop video models for this diagnosis task. However, the collection of ultrasound video datasets is much harder. In this paper, we explore the feasibility of enhancing the performance of ultrasound video c... | 2306.06877 | title_snapshot |
Paper1247 | Boundary Difference Over Union Loss For Medical Image Segmentation | [
"Fan Sun",
"Zhiming Luo",
"Shaozi Li"
] | https://conferences.miccai.org/2023/papers/093-Paper1247.html | null | 10.1007/978-3-031-43901-8_28 | https://rdcu.be/dnwDc | null | [
"Image Segmentation",
"Modalities - CT",
"Modalities - MRI"
] | [
"https://github.com/sunfan-bvb/BoundaryDoULoss"
] | [
"https://www.synapse.org/#!Synapse:syn3193805/wiki/217789",
"https://www.creatis.insa-lyon.fr/Challenge/acdc/"
] | null | null | Medical image segmentation is crucial for clinical diagnosis. However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation. Those that do exist often need to be used in combination with other losses and produce ineffective... | 2308.00220 | title_snapshot |
Paper2691 | Boundary-weighted logit consistency improves calibration of segmentation networks | [
"Neerav Karani",
"Neel Dey",
"Polina Golland"
] | https://conferences.miccai.org/2023/papers/094-Paper2691.html | null | 10.1007/978-3-031-43898-1_36 | https://rdcu.be/dnwBw | null | [
"Machine Learning - Uncertainty",
"Image Segmentation",
"Machine Learning - Data Efficient Learning",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - MRI"
] | [
"https://github.com/neerakara/BWCR"
] | [
"https://wiki.cancerimagingarchive.net/display/Public/NCI-ISBI+2013+Challenge+-+Automated+Segmentation+of+Prostate+Structures",
"https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html"
] | null | null | Neural network prediction probabilities and accuracy are often only weakly-correlated. Inherent label ambiguity in training data for image segmentation aggravates such miscalibration. We show that logit consistency across stochastic transformations acts as a spatially varying regularizer that prevents overconfident pre... | 2307.08163 | title_snapshot |
Paper0844 | Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI | [
"Lintao Zhang",
"Jinjian Wu",
"Lihong Wang",
"Li Wang",
"David C. Steffens",
"Shijun Qiu",
"Guy G. Potter",
"Mingxia Liu"
] | https://conferences.miccai.org/2023/papers/095-Paper0844.html | null | 10.1007/978-3-031-43993-3_11 | https://rdcu.be/dnwNc | null | [
"Clinical applications - Neuroimaging - Brain Development",
"Machine Learning - Transfer learning",
"Modalities - MRI"
] | [
"https://github.com/goodaycoder/BAR"
] | [] | null | null | Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specifi... | 2306.11837 | title_judge |
Paper1572 | BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis | [
"Pengshuai Zhang",
"Guangqi Wen",
"Peng Cao",
"Jinzhu Yang",
"Jinyu Zhang",
"Xizhe Zhang",
"Xinrong Zhu",
"Osmar R. Zaiane",
"Fei Wang"
] | https://conferences.miccai.org/2023/papers/096-Paper1572.html | null | 10.1007/978-3-031-43993-3_20 | https://rdcu.be/dnwNl | null | [
"Clinical applications - Neuroimaging - Functional Brain Networks",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning"
] | [
"https://github.com/IntelliDAL/Graph/tree/main/BrainUSL"
] | [] | null | null | The functional connectivity (FC) between brain regions is usually estimated through a statistical dependency method with functional magnetic resonance imaging (fMRI) data. It inevitably yields redundant and noise connections, limiting the performance of deep supervised models in brain disease diagnosis. Besides, the su... | null | null |
Paper1347 | Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network | [
"Bryar Shareef",
"Min Xian",
"Aleksandar Vakanski",
"Haotian Wang"
] | https://conferences.miccai.org/2023/papers/097-Paper1347.html | null | 10.1007/978-3-031-43901-8_33 | https://rdcu.be/dnwDH | null | [
"Image Segmentation",
"Clinical applications - Breast",
"Machine Learning - Attention models",
"Machine Learning - Model Generalizability / Federated Learning",
"Machine Learning - Transfer learning"
] | [] | [] | null | null | Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Although convolutional neural networks (CNNs) have demonstrated reliable performance in tumor classification, they have inherent limitations for modeling global and long-range dependencies due to the localized... | 2308.02101 | title_snapshot |
Paper2554 | Bridging ex-vivo training and intra-operative deployment for surgical margin assessment with Evidential Graph Transformer | [
"Amoon Jamzad",
"Fahimeh Fooladgar",
"Laura Connolly",
"Dilakshan Srikanthan",
"Ayesha Syeda",
"Martin Kaufmann",
"Kevin Y. M. Ren",
"Shaila Merchant",
"Jay Engel",
"Sonal Varma",
"Gabor Fichtinger",
"John F. Rudan",
"Parvin Mousavi"
] | https://conferences.miccai.org/2023/papers/098-Paper2554.html | null | 10.1007/978-3-031-43990-2_53 | https://rdcu.be/dnwL8 | null | [
"Clinical applications - Oncology",
"Clinical applications - Breast",
"Computer Aided Diagnosis",
"Machine Learning - Attention models",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Uncertainty",
"Modalities - other",
"Surgical Data Science"
] | [
"https://github.com/med-i-lab/evidential_graph_transformers/"
] | [] | null | null | PURPOSE: The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first ti... | null | null |
Paper1146 | Building A Bridge: Close The Domain Gap in CT Metal Artifact Reduction | [
"Tao Wang",
"Hui Yu",
"Yan Liu",
"Huaiqiang Sun",
"Yi Zhang"
] | https://conferences.miccai.org/2023/papers/099-Paper1146.html | null | 10.1007/978-3-031-43999-5_20 | https://rdcu.be/dnwwy | null | [
"Image Reconstruction"
] | [] | [] | null | null | Metal artifacts in computed tomography (CT) degrade the imaging quality, leading to a negative impact on the clinical diagnosis. Empowered by medical big data, many DL-based approaches have been proposed for metal artifact reduction (MAR). In supervised MAR methods, models are usually trained on simulated data and appl... | null | null |
Paper2579 | Can point cloud networks learn statistical shape models of anatomies? | [
"Jadie Adams",
"Shireen Y. Elhabian"
] | https://conferences.miccai.org/2023/papers/100-Paper2579.html | null | 10.1007/978-3-031-43907-0_47 | https://rdcu.be/dnwdt | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Computational Anatomy and Physiology"
] | [
"https://github.com/jadie1/PointCompletionSSM"
] | [
"https://github.com/jadie1/PointCompletionSSM"
] | null | null | Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies. However, traditional correspondence-based SSM generation methods have a prohibitive inference process and require complete geometric proxies (e.g., high-resolution binary volumes ... | 2305.05610 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.