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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Paper1347 | 3D CVT-GAN: A 3D Convolutional Vision Transformer-GAN for PET Reconstruction | [
"Pinxian Zeng",
"Luping Zhou",
"Chen Zu",
"Xinyi Zeng",
"Zhengyang Jiao",
"Xi Wu",
"Jiliu Zhou",
"Dinggang Shen",
"Yan Wang"
] | https://conferences.miccai.org/2022/papers/001-Paper1347.html | null | 10.1007/978-3-031-16446-0_49 | https://rdcu.be/cVRTI | null | [
"Image Reconstruction",
"Modalities - PET/SPECT",
"Organ - Brain"
] | [
"https://github.com/Aru321/CVTGAN"
] | [] | null | null | To obtain high-quality positron emission tomography (PET) scans while reducing potential radiation hazards brought to patients, various generative adversarial network (GAN)-based methods have been developed to reconstruct high-quality standard-dose PET (SPET) images from low-dose PET (LPET) images. However, due to the ... | null | null |
Paper1233 | 3D Global Fourier Network for Alzheimer’s Disease Diagnosis using Structural MRI | [
"Shengjie Zhang",
"Xiang Chen",
"Bohan Ren",
"Haibo Yang",
"Ziqi Yu",
"Xiao-Yong Zhang",
"Yuan Zhou"
] | https://conferences.miccai.org/2022/papers/002-Paper1233.html | null | 10.1007/978-3-031-16431-6_4 | https://rdcu.be/cVD4L | null | [
"Computer Aided Diagnosis",
"Modalities - MRI",
"Outcome/disease prediction"
] | [
"https://github.com/qbmizsj/GFNet"
] | [
"https://adni.loni.usc.edu"
] | null | null | Deep learning models, such as convolutional neural networks and self-attention mechanisms, have been shown to be effective in computer-aided diagnosis (CAD) of Alzheimer’s disease (AD) using structural magnetic resonance imaging (sMRI). Most of them use spatial convolutional filters to learn local information from the ... | null | null |
Paper1512 | 4D-OR: Semantic Scene Graphs for OR Domain Modeling | [
"Ege Özsoy",
"Evin Pınar Örnek",
"Ulrich Eck",
"Tobias Czempiel",
"Federico Tombari",
"Nassir Navab"
] | https://conferences.miccai.org/2022/papers/003-Paper1512.html | null | 10.1007/978-3-031-16449-1_45 | https://rdcu.be/cVRXk | null | [
"Surgical Scene Understanding",
"Surgical Skill and Work Flow Analysis"
] | [
"https://github.com/egeozsoy/4D-OR"
] | [
"https://github.com/egeozsoy/4D-OR"
] | null | null | Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts are capable of understanding all the links and interactions in such a demanding environment. This paper aims to bring the community one step clo... | 2203.11937 | title_snapshot |
Paper2655 | A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000 | [
"Sontje Ihler",
"Felix Kuhnke",
"Svenja Spindeldreier"
] | https://conferences.miccai.org/2022/papers/004-Paper2655.html | null | 10.1007/978-3-031-16431-6_62 | https://rdcu.be/cVD7i | null | [
"Computer Aided Diagnosis",
"Machine Learning - Data efficient Learning",
"Machine Learning - Transfer learning",
"Modalities - other",
"Organ - Lung"
] | [
"https://gitlab.uni-hannover.de/sontje.ihler/chexpert5000"
] | [
"https://stanfordmlgroup.github.io/competitions/chexpert/"
] | null | null | Computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets. Although these datasets are still small compared to natural image datasets like ImageNet1... | 2302.06684 | title_snapshot |
Paper1525 | A Deep-Discrete Learning Framework for Spherical Surface Registration | [
"Mohamed A. Suliman",
"Logan Z. J. Williams",
"Abdulah Fawaz",
"Emma C. Robinson"
] | https://conferences.miccai.org/2022/papers/005-Paper1525.html | null | 10.1007/978-3-031-16446-0_12 | https://rdcu.be/cVRSS | null | [
"Image Registration",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning"
] | [
"https://github.com/mohamedasuliman/DDR/"
] | [
"https://db.humanconnectome.org/"
] | null | null | Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is performed by optimizing a complex objective similarity function, leading to long run times. This contribu... | 2203.12999 | title_snapshot |
Paper1155 | A Geometry-Constrainted Deformable Attention Network for Aortic Segmentation | [
"Weiyuan Lin",
"Hui Liu",
"Lin Gu",
"Zhifan Gao"
] | https://conferences.miccai.org/2022/papers/006-Paper1155.html | null | 10.1007/978-3-031-16443-9_28 | https://rdcu.be/cVRyG | null | [
"Image Segmentation",
"Modalities - CT",
"Organ - Vessel"
] | [] | [] | null | null | Morphological segmentation of the aorta is significant for aortic diagnosis, intervention, and prognosis. However, it is difficult for existing methods to achieve the continuity of spatial information and the integrity of morphological extraction, due to the gradually variable and irregular geometry of the aorta in the... | null | null |
Paper1247 | A Hybrid Propagation Network for Interactive Volumetric Image Segmentation | [
"Luyue Shi",
"Xuanye Zhang",
"Yunbi Liu",
"Xiaoguang Han"
] | https://conferences.miccai.org/2022/papers/007-Paper1247.html | null | 10.1007/978-3-031-16440-8_64 | https://rdcu.be/cVRwR | null | [
"Image Segmentation",
"Modalities - CT"
] | [
"https://github.com/luyueshi/Hybrid-Propagation"
] | [
"http://medicaldecathlon.com/",
"https://kits19.grand-challenge.org/"
] | null | null | Interactive segmentation is of great importance in clinical practice for correcting and refining the automated segmentation by involving additional user hints, e.g., scribbles and clicks. Currently, interactive segmentation methods for 2D medical images are well studied, while seldom works are conducted on 3D medical v... | null | null |
Paper0063 | A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis | [
"Wanyu Bian",
"Qingchao Zhang",
"Xiaojing Ye",
"Yunmei Chen"
] | https://conferences.miccai.org/2022/papers/008-Paper0063.html | null | 10.1007/978-3-031-16446-0_34 | https://rdcu.be/cVRTt | null | [
"Image Reconstruction",
"Machine Learning - Other",
"Organ - Brain"
] | [] | [
"https://www.med.upenn.edu/sbia/brats2018/data.html"
] | null | null | Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source moda... | 2204.03804 | title_snapshot |
Paper1374 | A Medical Semantic-Assisted Transformer for Radiographic Report Generation | [
"Zhanyu Wang",
"Mingkang Tang",
"Lei Wang",
"Xiu Li",
"Luping Zhou"
] | https://conferences.miccai.org/2022/papers/009-Paper1374.html | null | 10.1007/978-3-031-16437-8_63 | https://rdcu.be/cVRuO | null | [
"Computer Aided Diagnosis",
"Modalities - Text (clinical/radiology reports)"
] | [
"https://github.com/zwan0839/MSAT"
] | [
"https://drive.google.com/file/d/1DS6NYirOXQf8qYieSVMvqNwuOlgAbM_E/view"
] | null | null | Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images. Despite the recent progress in this field, there are still many challenges at least in the following aspects. First, radiographic images are... | 2208.10358 | title_snapshot |
Paper1785 | A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images | [
"Wentian Zhang",
"Xu Sun",
"Yuexiang Li",
"Haozhe Liu",
"Nanjun He",
"Feng Liu",
"Yefeng Zheng"
] | https://conferences.miccai.org/2022/papers/010-Paper1785.html | null | 10.1007/978-3-031-16434-7_63 | https://rdcu.be/cVRsv | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Image Reconstruction",
"Organ - Eye",
"Outcome/disease prediction"
] | [
"https://github.com/WentianZhang-ML/WDMT-Net"
] | [] | null | null | By introducing the skip connection to bridge the semantic gap between encoder and decoder, U-shape architecture has been proven to be effective for recovering fine-grained details in dense prediction tasks. However, such a mechanism cannot be directly applied to reconstruction-based anomaly detection, since the skip co... | null | null |
Paper1016 | A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos | [
"Zhi Lin",
"Junhao Lin",
"Lei Zhu",
"Huazhu Fu",
"Jing Qin",
"Liansheng Wang"
] | https://conferences.miccai.org/2022/papers/011-Paper1016.html | null | 10.1007/978-3-031-16437-8_59 | https://rdcu.be/cVRuK | null | [
"Modalities - Video",
"Machine Learning - Other",
"Modalities - Ultrasound",
"Organ - Breast"
] | [
"https://github.com/jhl-Det/CVA-Net"
] | [
"https://pan.baidu.com/s/1yYME7-DvvIEZzCb72NXaJA?pwd=jnie"
] | null | null | Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 video... | 2207.00141 | title_snapshot |
Paper0202 | A Novel Deep Learning System for Breast Lesion Risk Stratification in Ultrasound Images | [
"Ting Liu",
"Xing An",
"Yanbo Liu",
"Yuxi Liu",
"Bin Lin",
"Runzhou Jiang",
"Wenlong Xu",
"Longfei Cong",
"Lei Zhu"
] | https://conferences.miccai.org/2022/papers/012-Paper0202.html | null | 10.1007/978-3-031-16437-8_45 | https://rdcu.be/cVRuw | null | [
"Computer Aided Diagnosis",
"Modalities - Ultrasound",
"Organ - Breast",
"Organ - Tumor"
] | [] | [] | null | null | This paper presents a novel deep learning system to classify breast lesions in ultrasound images into benign and malignant and into Breast Imaging Reporting and Data System (BI-RADS) six categories simultaneously. A multitask soft label generating architecture is proposed to improve the classification performance, in w... | null | null |
Paper0975 | A Novel Fusion Network for Morphological Analysis of Common Iliac Artery | [
"Meng Song",
"Shi-Qi Liu",
"Xiao-Liang Xie",
"Xiao-Hu Zhou",
"Zeng-Guang Hou",
"Yan-Jie Zhou",
"Xi-Yao Ma"
] | https://conferences.miccai.org/2022/papers/013-Paper0975.html | null | 10.1007/978-3-031-16449-1_6 | https://rdcu.be/cVRUM | null | [
"Image Segmentation",
"Computer Aided Diagnosis",
"Image Analysis in Robot-Assisted Surgery",
"Modalities - other",
"Organ - Vessel"
] | [
"https://github.com/SongCASIA/FTU_Net"
] | [] | null | null | In endovascular interventional therapy, automatic common iliac artery morphological analysis can help physicians plan surgical procedures and assist in the selection of appropriate stents to improve surgical safety. However, different people have distinct blood vessel shapes, and many patients have severe malformations... | null | null |
Paper1405 | A Novel Knowledge Keeper Network for 7T-Free But 7T-Guided Brain Tissue Segmentation | [
"Jieun Lee",
"Kwanseok Oh",
"Dinggang Shen",
"Heung-Il Suk"
] | https://conferences.miccai.org/2022/papers/014-Paper1405.html | null | 10.1007/978-3-031-16443-9_32 | https://rdcu.be/cVRyM | null | [
"Machine Learning - Transfer learning",
"Image Segmentation",
"Modalities - MRI",
"Organ - Brain"
] | [
"https://github.com/2jieun2/knowledge_keeper"
] | [
"https://www.nitrc.org/projects/ibsr"
] | null | null | An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clini... | null | null |
Paper1881 | A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models | [
"Anthony Vento",
"Qingyu Zhao",
"Robert Paul",
"Kilian M. Pohl",
"Ehsan Adeli"
] | https://conferences.miccai.org/2022/papers/015-Paper1881.html | null | 10.1007/978-3-031-16437-8_37 | https://rdcu.be/cVRtn | null | [
"Outcome/disease prediction",
"Computer Aided Diagnosis",
"Modalities - MRI",
"Organ - Brain"
] | [
"https://github.com/vento99"
] | [
"https://github.com/mlu355/MetadataNorm/blob/main/synthetic_dataset.py",
"https://adni.loni.usc.edu/"
] | null | null | Translating machine learning algorithms into clinical applications requires addressing challenges related to interpretability, such as accounting for the effect of confounding variables (or metadata). Confounding variables affect the relationship between input training data and target outputs. When we train a model on ... | 2207.04607 | title_snapshot |
Paper2636 | A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects | [
"Chang Gao",
"Shu-Fu Shih",
"J. Paul Finn",
"Xiaodong Zhong"
] | https://conferences.miccai.org/2022/papers/016-Paper2636.html | null | 10.1007/978-3-031-16446-0_69 | https://rdcu.be/cVRUd | null | [
"Image Reconstruction",
"Machine Learning - Data efficient Learning",
"Machine Learning - Model Generalizability",
"Modalities - MRI",
"Organ - Abdomen"
] | [] | [] | null | null | The recent development of deep learning combined with compressed sensing enables fast reconstruction of undersampled MR images and has achieved state-of-the-art performance for Cartesian k-space trajectories. However, non-Cartesian trajectories such as the radial trajectory need to be transformed onto a Cartesian grid ... | 2206.07219 | title_snapshot |
Paper0545 | A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation | [
"Himashi Peiris",
"Munawar Hayat",
"Zhaolin Chen",
"Gary Egan",
"Mehrtash Harandi"
] | https://conferences.miccai.org/2022/papers/017-Paper0545.html | null | 10.1007/978-3-031-16443-9_16 | https://rdcu.be/cVRyu | null | [
"Image Segmentation",
"Modalities - MRI",
"Organ - Brain",
"Organ - Tumor"
] | [
"https://github.com/himashi92/VT-UNet"
] | [
"http://medicaldecathlon.com/"
] | null | null | We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode... | 2111.13300 | title_snapshot |
Paper0114 | A Self-Guided Framework for Radiology Report Generation | [
"Jun Li",
"Shibo Li",
"Ying Hu",
"Huiren Tao"
] | https://conferences.miccai.org/2022/papers/018-Paper0114.html | null | 10.1007/978-3-031-16452-1_56 | https://rdcu.be/cVVqc | null | [
"Modalities - Text (clinical/radiology reports)",
"Computer Aided Diagnosis",
"Organ - Lung"
] | [
"https://github.com/LijunRio/A-Self-Guided-Framework"
] | [
"https://openi.nlm.nih.gov/"
] | null | null | Automatic radiology report generation is essential to computer-aided diagnosis. Through the success of image captioning, medical report generation has been achievable. However, the lack of annotated disease labels is still the bottleneck of this area. In addition, the image-text data bias problem and complex sentences ... | 2206.09378 | title_snapshot |
Paper0199 | A Sense of Direction in Biomedical Neural Networks | [
"Zewen Liu",
"Timothy F. Cootes"
] | https://conferences.miccai.org/2022/papers/019-Paper0199.html | null | 10.1007/978-3-031-16443-9_8 | https://rdcu.be/cVRye | null | [
"Image Segmentation",
"Machine Learning - Interpretability / Explainability",
"Organ - Eye",
"Organ - Vessel"
] | [
"https://github.com/Zewen-Liu/MASC-Unit"
] | [
"https://blogs.kingston.ac.uk/retinal/chasedb1/",
"https://drive.grand-challenge.org/",
"https://monuseg.grand-challenge.org/Data/"
] | null | null | We describe an approach to making a model be aware of not only intensity but also properties such as direction and scale during forward propagation. Such properties are important in when analysing images containing curvilinear structures such as vessels or fibres. We propose the General Multi-Angle Scale Convolution (G... | null | null |
Paper0395 | A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT | [
"Stefan Ploner",
"Siyu Chen",
"Jungeun Won",
"Lennart Husvogt",
"Katharina Breininger",
"Julia Schottenhamml",
"James Fujimoto",
"Andreas Maier"
] | https://conferences.miccai.org/2022/papers/020-Paper0395.html | null | 10.1007/978-3-031-16434-7_50 | https://rdcu.be/cVRsi | null | [
"Image Registration",
"Modalities - Biophotonics",
"Organ - Eye"
] | [] | [] | null | null | Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during ... | 2209.07232 | title_snapshot |
Paper0893 | A Transformer-Based Iterative Reconstruction Model for Sparse-View CT Reconstruction | [
"Wenjun Xia",
"Ziyuan Yang",
"Qizheng Zhou",
"Zexin Lu",
"Zhongxian Wang",
"Yi Zhang"
] | https://conferences.miccai.org/2022/papers/021-Paper0893.html | null | 10.1007/978-3-031-16446-0_75 | https://rdcu.be/cVRUj | null | [
"Image Reconstruction",
"Modalities - CT"
] | [
"https://github.com/Deep-Imaging-Group/RegFormer"
] | [] | null | null | Sparse-view computed tomography (CT) is one of the primary means to reduce the radiation risk. But the reconstruction of sparse-view CT will be contaminated by severe artifacts. By carefully designing the regularization terms, the iterative reconstruction (IR) algorithm can achieve promising results. With the introduct... | null | null |
Paper0036 | AANet: Artery-Aware Network for Pulmonary Embolism Detection in CTPA Images | [
"Jia Guo",
"Xinglong Liu",
"Yinan Chen",
"Shaoting Zhang",
"Guangyu Tao",
"Hong Yu",
"Huiyuan Zhu",
"Wenhui Lei",
"Huiqi Li",
"Na Wang"
] | https://conferences.miccai.org/2022/papers/022-Paper0036.html | null | 10.1007/978-3-031-16431-6_45 | https://rdcu.be/cVD61 | null | [
"Computer Aided Diagnosis",
"Image Segmentation",
"Machine Learning - Data efficient Learning",
"Modalities - CT",
"Organ - Lung"
] | [
"https://github.com/guojiajeremy/AANet"
] | [
"https://ieee-dataport.org/open-access/cad-pe",
"https://www.kaggle.com/datasets/andrewmvd/pulmonary-embolism-in-ct-images"
] | null | null | Pulmonary embolism (PE) is life-threatening and computed tomography pulmonary angiography (CTPA) is the best diagnostic techniques in clinics. However, PEs usually appear as dark spots among the bright regions of blood arteries in CTPA images, which can be very similar with veins that are less bright and soft tissues. ... | null | null |
Paper2394 | Accelerated pseudo 3D dynamic speech MR imaging at 3T using unsupervised deep variational manifold learning | [
"Rushdi Zahid Rusho",
"Qing Zou",
"Wahidul Alam",
"Subin Erattakulangara",
"Mathews Jacob",
"Sajan Goud Lingala"
] | https://conferences.miccai.org/2022/papers/023-Paper2394.html | null | 10.1007/978-3-031-16446-0_66 | https://rdcu.be/cVRUa | null | [
"Image Reconstruction",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Modalities - MRI",
"Organ - Other",
"Visualisation in Biomedical Imaging"
] | [
"https://github.com/rushdi-rusho/varMRI"
] | [
"https://github.com/rushdi-rusho/varMRI/tree/main/SpeechDatasets"
] | null | null | Magnetic resonance imaging (MRI) of vocal tract shaping and surrounding articulators during speaking is a powerful tool in several application areas such as understanding language disorder, informing treatment plans in oro-pharyngeal cancers. However, this is a challenging task due to fundamental tradeoffs between spat... | null | null |
Paper2344 | Accurate and Explainable Image-based Prediction Using a Lightweight Generative Model | [
"Chiara Mauri",
"Stefano Cerri",
"Oula Puonti",
"Mark Mühlau",
"Koen Van Leemput"
] | https://conferences.miccai.org/2022/papers/024-Paper2344.html | null | 10.1007/978-3-031-16452-1_43 | https://rdcu.be/cVVpY | null | [
"Outcome/disease prediction",
"Machine Learning - Data efficient Learning",
"Machine Learning - Interpretability / Explainability",
"Modalities - MRI",
"Organ - Brain"
] | [] | [
"https://www.ukbiobank.ac.uk"
] | null | null | Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject’s age, from individual brain scans. Although the field has focused strongly on nonlinear discriminative methods using deep learning, here we explore whether linear generative techniques can be used as practical... | null | null |
Paper0249 | Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers | [
"Youbao Tang",
"Ning Zhang",
"Yirui Wang",
"Shenghua He",
"Mei Han",
"Jing Xiao",
"Ruei-Sung Lin"
] | https://conferences.miccai.org/2022/papers/025-Paper0249.html | null | 10.1007/978-3-031-16440-8_51 | https://rdcu.be/cVRwC | null | [
"Image Segmentation",
"Computer Aided Diagnosis",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Modalities - CT"
] | [] | [
"https://nihcc.app.box.com/v/DeepLesion",
"https://github.com/JimmyCai91/DLT"
] | null | null | Automatically measuring lesion/tumor size with RECIST (Response Evaluation Criteria In Solid Tumors) diameters and segmentation is important for computer-aided diagnosis. Although it has been studied in recent years, there is still space to improve its accuracy and robustness, such as (1) enhancing features by incorpor... | 2208.13113 | title_snapshot |
Paper1270 | Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner | [
"Zhenwei Wang",
"Yifan Lv",
"Mengshen He",
"Enjie Ge",
"Ning Qiang",
"Bao Ge"
] | https://conferences.miccai.org/2022/papers/026-Paper1270.html | null | 10.1007/978-3-031-16431-6_14 | https://rdcu.be/cVD4V | null | [
"Organ - Brain",
"Image Segmentation",
"Modalities - MRI"
] | [
"https://github.com/Garand0o0/FiberTractSegmentation"
] | [] | null | null | Fiber tract segmentation is a prerequisite for the tract-based statistical analysis and plays a crucial role in understanding brain structure and function. The previous researches mainly consist of two steps: defining and computing the similarity features of fibers, and then adopting machine learning algorithm for clus... | null | null |
Paper0159 | ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training | [
"Xiaofeng Liu",
"Fangxu Xing",
"Nadya Shusharina",
"Ruth Lim",
"C.-C. Jay Kuo",
"Georges El Fakhri",
"Jonghye Woo"
] | https://conferences.miccai.org/2022/papers/027-Paper0159.html | null | 10.1007/978-3-031-16443-9_7 | https://rdcu.be/cVRyd | null | [
"Image Segmentation",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Machine Learning - Transfer learning",
"Modalities - MRI",
"Organ - Brain"
] | [] | [] | null | null | Unsupervised domain adaptation (UDA) has been vastly explored to address domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain. Recent literature, however, has indicated that the performance is still far from satisfac... | 2206.02288 | title_snapshot |
Paper2509 | Adaptation of Surgical Activity Recognition Models Across Operating Rooms | [
"Ali Mottaghi",
"Aidean Sharghi",
"Serena Yeung",
"Omid Mohareri"
] | https://conferences.miccai.org/2022/papers/028-Paper2509.html | null | 10.1007/978-3-031-16449-1_51 | https://rdcu.be/cVRXp | null | [
"Surgical Skill and Work Flow Analysis",
"Machine Learning - Model Generalizability",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Surgical Data Science",
"Surgical Scene Understanding"
] | [] | [] | null | null | Automatic surgical activity recognition enables more intelligent surgical devices and a more efficient workflow. Integration of such technology in new operating rooms has the potential to improve care delivery to patients and decrease costs. Recent works have achieved a promising performance on surgical activity recogn... | 2207.03083 | title_snapshot |
Paper2006 | Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts | [
"Alexander Bigalke",
"Lasse Hansen",
"Mattias P. Heinrich"
] | https://conferences.miccai.org/2022/papers/029-Paper2006.html | null | 10.1007/978-3-031-16446-0_27 | https://rdcu.be/cVRS9 | null | [
"Image Registration",
"Machine Learning - Transfer learning",
"Organ - Lung"
] | [
"https://github.com/multimodallearning/registration-da-mean-teacher"
] | [
"https://learn2reg.grand-challenge.org/Learn2Reg2021/",
"https://med.emory.edu/departments/radiation-oncology/research-laboratories/deformable-image-registration/downloads-and-reference-data/index.html"
] | null | null | Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. While... | 2207.00371 | title_snapshot |
Paper0272 | Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images | [
"Pak-Hei Yeung",
"Moska Aliasi",
"Monique Haak",
"the INTERGROWTH-21st Consortium",
"Weidi Xie",
"Ana I. L. Namburete"
] | https://conferences.miccai.org/2022/papers/030-Paper0272.html | null | 10.1007/978-3-031-16440-8_20 | https://rdcu.be/cVRvL | null | [
"Modalities - Ultrasound",
"Computer Aided Diagnosis",
"Machine Learning - Transfer learning",
"Organ - Brain"
] | [
"https://github.com/pakheiyeung/AdLocUI"
] | [] | null | null | Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Loca... | 2209.05477 | title_snapshot |
Paper2848 | AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching | [
"Khanh Nguyen",
"Huy Hoang Nguyen",
"Aleksei Tiulpin"
] | https://conferences.miccai.org/2022/papers/031-Paper2848.html | null | 10.1007/978-3-031-16452-1_69 | https://rdcu.be/cVVqq | null | [
"Machine Learning - Other",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Modalities - other",
"Organ - Breast",
"Organ - Lung",
"Organ - Musculoskeleton"
] | [
"https://github.com/Oulu-IMEDS/AdaTriplet"
] | [
"https://nda.nih.gov/oai/",
"https://nihcc.app.box.com/v/ChestXray-NIHCC"
] | null | null | This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and... | 2205.02849 | title_snapshot |
Paper1371 | Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI | [
"Hritam Basak",
"Sagnik Ghosal",
"Ram Sarkar"
] | https://conferences.miccai.org/2022/papers/032-Paper1371.html | null | 10.1007/978-3-031-16452-1_22 | https://rdcu.be/cVRY4 | null | [
"Image Segmentation",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning"
] | [] | [
"https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html",
"http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/"
] | null | null | Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate thi... | 2209.00123 | title_snapshot |
Paper0332 | Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation | [
"Yanwu Xu",
"Shaoan Xie",
"Maxwell Reynolds",
"Matthew Ragoza",
"Mingming Gong",
"Kayhan Batmanghelich"
] | https://conferences.miccai.org/2022/papers/033-Paper0332.html | null | 10.1007/978-3-031-16449-1_64 | https://rdcu.be/cVRXC | null | [
"Machine Learning - Transfer learning",
"Image Segmentation"
] | [] | [] | null | null | An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training.... | 2206.13737 | title_snapshot |
Paper1829 | Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs | [
"Prashant Pandey",
"Aleti Vardhan",
"Mustafa Chasmai",
"Tanuj Sur",
"Brejesh Lall"
] | https://conferences.miccai.org/2022/papers/034-Paper1829.html | null | 10.1007/978-3-031-16452-1_8 | https://rdcu.be/cVRYL | null | [
"Machine Learning - Data efficient Learning",
"Image Segmentation",
"Machine Learning - Model Generalizability",
"Modalities - CT",
"Modalities - MRI",
"Organ - Abdomen",
"Organ - Lung"
] | [
"https://github.com/prinshul/Prototype_NeuralODE_Adv_Attack"
] | [] | null | null | Few-shot Learning (FSL) methods are being adopted in settings where data is not abundantly available. This is especially seen in medical domains where the annotations are expensive to obtain. Deep Neural Networks have been shown to be vulnerable to adversarial attacks. This is even more severe in the case of FSL due to... | 2210.03429 | title_snapshot |
Paper1248 | Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound | [
"Yuxin Zou",
"Haoran Dou",
"Yuhao Huang",
"Xin Yang",
"Jikuan Qian",
"Chaojiong Zhen",
"Xiaodan Ji",
"Nishant Ravikumar",
"Guoqiang Chen",
"Weijun Huang",
"Alejandro F. Frangi",
"Dong Ni"
] | https://conferences.miccai.org/2022/papers/035-Paper1248.html | null | 10.1007/978-3-031-16440-8_29 | https://rdcu.be/cVRvU | null | [
"Machine Learning - Reinforcement learning",
"Modalities - Ultrasound",
"Organ - Fetal",
"Organ - Other"
] | [] | [] | null | null | Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation var... | 2207.00475 | title_snapshot |
Paper1552 | Aggregative Self-Supervised Feature Learning from Limited Medical Images | [
"Jiuwen Zhu",
"Yuexiang Li",
"Lian Ding",
"S. Kevin Zhou"
] | https://conferences.miccai.org/2022/papers/036-Paper1552.html | null | 10.1007/978-3-031-16452-1_6 | https://rdcu.be/cVRYJ | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning"
] | [] | [] | null | null | Limited training data and annotation shortage are the main challenges for the development of automated medical image analysis systems. As a potential solution, self-supervised learning (SSL) causes an increasing attention from the community. The key part in SSL is its proxy task that defines the supervisory signals and... | 2012.07477 | title_judge |
Paper1305 | An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-Lesions | [
"He Li",
"Yutaro Iwamoto",
"Xianhua Han",
"Lanfen Lin",
"Hongjie Hu",
"Yen-Wei Chen"
] | https://conferences.miccai.org/2022/papers/037-Paper1305.html | null | 10.1007/978-3-031-16452-1_21 | https://rdcu.be/cVRY3 | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Image Reconstruction",
"Modalities - CT",
"Organ - Abdomen",
"Organ - Tumor"
] | [] | [] | null | null | Anomaly detection using an unsupervised learning scheme has become a challenging research topic. Unsupervised learning requires only unlabeled normal data for training and can detect anomalies in unseen testing data. In this paper, we propose an unsupervised liver lesion detection framework based on generative adversar... | null | null |
Paper1415 | An adaptive network with extragradient for diffusion MRI-based microstructure estimation | [
"Tianshu Zheng",
"Weihao Zheng",
"Yi Sun",
"Yi Zhang",
"Chuyang Ye",
"Dan Wu"
] | https://conferences.miccai.org/2022/papers/038-Paper1415.html | null | 10.1007/978-3-031-16431-6_15 | https://rdcu.be/cVD4X | null | [
"Modalities - MRI",
"Machine Learning - Other",
"Organ - Brain"
] | [] | [] | null | null | Diffusion MRI (dMRI) is a powerful tool for probing tissue microstructural properties. However, advanced dMRI models are commonly nonlinear and complex, which requires densely sampled q-space and is prone to estimation errors. This problem can be resolved using deep learning techniques, especially optimization-based ne... | null | null |
Paper2756 | An Advanced Deep Learning Framework for Video-based Diagnosis of ASD | [
"Miaomiao Cai",
"Mingxing Li",
"Zhiwei Xiong",
"Pengju Zhao",
"Enyao Li",
"Jiulai Tang"
] | https://conferences.miccai.org/2022/papers/039-Paper2756.html | null | 10.1007/978-3-031-16440-8_42 | https://rdcu.be/cVRwt | null | [
"Computer Aided Diagnosis",
"Modalities - Video",
"Outcome/disease prediction"
] | [
"https://github.com/xiaotaiyangcmm/DASD"
] | [
"https://github.com/xiaotaiyangcmm/DASD"
] | null | null | Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders, which impairs the communication and interaction ability of patients. Intensive intervention in early ASD can effectively improve symptoms, so the diagnosis of ASD children receives significant attention. However, clinical assessment ... | null | null |
Paper0031 | An End-to-End Combinatorial Optimization Method for R-band Chromosome Recognition with Grouping Guided Attention | [
"Chao Xia",
"Jiyue Wang",
"Yulei Qin",
"Yun Gu",
"Bing Chen",
"Jie Yang"
] | https://conferences.miccai.org/2022/papers/040-Paper0031.html | null | 10.1007/978-3-031-16440-8_1 | https://rdcu.be/cVRvl | null | [
"Modalities - Microscopy",
"Computer Aided Diagnosis",
"Outcome/disease prediction",
"Population Imaging and Imaging Genetics"
] | [
"https://github.com/xiabc612/R-band-chromosome-recognition"
] | [] | null | null | Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. Existing computer-aided chromosome recognition methods mainly focus on better feature representation of individual chromosomes while neglecting the fact that chromosomes fro... | null | null |
Paper0049 | An Inclusive Task-Aware Framework for Radiology Report Generation | [
"Lin Wang",
"Munan Ning",
"Donghuan Lu",
"Dong Wei",
"Yefeng Zheng",
"Jie Chen"
] | https://conferences.miccai.org/2022/papers/041-Paper0049.html | null | 10.1007/978-3-031-16452-1_54 | https://rdcu.be/cVVqa | null | [
"Modalities - Text (clinical/radiology reports)"
] | [
"https://github.com/Reremee/ITA"
] | [] | null | null | To avoid the tedious and laborious radiology report writing, the automatic generation of radiology reports has drawn great attention recently. Previous studies attempted to directly transfer the image captioning method to radiology report generation given the apparent similarity between these two tasks. Although these ... | null | null |
Paper1549 | An Optimal Control Problem for Elastic Registration and Force Estimation in Augmented Surgery | [
"Guillaume Mestdagh",
"Stéphane Cotin"
] | https://conferences.miccai.org/2022/papers/042-Paper1549.html | null | 10.1007/978-3-031-16449-1_8 | https://rdcu.be/cVRUO | null | [
"Image Registration",
"Image-Guided Interventions",
"Interventional Simulation Systems",
"Organ - Abdomen"
] | [
"https://github.com/gmestdagh/adjoint-elastic-registration"
] | [] | null | null | The nonrigid alignment between a pre-operative biomechanical model and an intra-operative observation is a critical step to track the motion of a soft organ in augmented surgery. While many elastic registration procedures introduce artificial forces into the direct physical model to drive the registration, we propose i... | 2206.10931 | title_snapshot |
Paper1625 | Analyzing and Improving Low Dose CT Denoising Network via HU Level Slicing | [
"Sutanu Bera",
"Prabir Kumar Biswas"
] | https://conferences.miccai.org/2022/papers/043-Paper1625.html | null | 10.1007/978-3-031-16446-0_56 | https://rdcu.be/cVRTZ | null | [
"Image Reconstruction",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Model Generalizability",
"Modalities - CT"
] | [] | [] | null | null | The deep convolutional neural network has been extensively studied for medical images denoising, specifically for low dose CT(LDCT) denoising. However, most of them disregard that medical images have a large dynamic range. After normalizing the input image, the difference between two nearby HU levels becomes minimal; f... | null | null |
Paper1703 | Analyzing Brain Structural Connectivity as Continuous Random Functions | [
"William Consagra",
"Martin Cole",
"Zhengwu Zhang"
] | https://conferences.miccai.org/2022/papers/044-Paper1703.html | null | 10.1007/978-3-031-16452-1_27 | https://rdcu.be/cVRY9 | null | [
"Machine Learning - Data efficient Learning",
"Modalities - MRI",
"Organ - Brain"
] | [
"https://github.com/sbci-brain/SBCI_Modeling_FPCA"
] | [
"https://db.humanconnectome.org"
] | null | null | This work considers a continuous framework to characterize the population-level variability of structural connectivity. Our framework assumes the observed white matter fiber tract endpoints are driven by a latent random function defined over a product manifold domain. To overcome the computational challenges of analyzi... | 2206.11191 | title_snapshot |
Paper1726 | Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays | [
"Ke Yu",
"Shantanu Ghosh",
"Zhexiong Liu",
"Christopher Deible",
"Kayhan Batmanghelich"
] | https://conferences.miccai.org/2022/papers/045-Paper1726.html | null | 10.1007/978-3-031-16443-9_63 | https://rdcu.be/cVRzh | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Computer Aided Diagnosis",
"Machine Learning - Data efficient Learning",
"Machine Learning - Interpretability / Explainability",
"Modalities - Text (clinical/radiology reports)",
"Organ - Lung",
"Outcome/disease prediction"... | [
"https://github.com/batmanlab/AGXNet"
] | [] | null | null | Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task. Leveraging weak supervision from readily available data such as radiology reports can compensate lack of large-scale data for anomaly detection methods. However, most of the current methods only use image-le... | 2206.12704 | title_snapshot |
Paper2430 | Anomaly-aware multiple instance learning for rare anemia disorder classification | [
"Salome Kazeminia",
"Ario Sadafi",
"Asya Makhro",
"Anna Bogdanova",
"Shadi Albarqouni",
"Carsten Marr"
] | https://conferences.miccai.org/2022/papers/046-Paper2430.html | null | 10.1007/978-3-031-16452-1_33 | https://rdcu.be/cVVpG | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Computer Aided Diagnosis",
"Machine Learning - Interpretability / Explainability",
"Modalities - Microscopy"
] | [
"https://github.com/marrlab/Anomaly-aware-MIL"
] | [] | null | null | Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to be an effective solution, yet it suffers from low accuracy and limited explainability. Although the inclusion of attention mechanisms has a... | 2207.01742 | title_snapshot |
Paper1683 | Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians | [
"Matthew MacPherson",
"Keerthini Muthuswamy",
"Ashik Amlani",
"Charles Hutchinson",
"Vicky Goh",
"Giovanni Montana"
] | https://conferences.miccai.org/2022/papers/047-Paper1683.html | null | 10.1007/978-3-031-16449-1_25 | https://rdcu.be/cVRU5 | null | [
"Machine Learning - Other",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Model Generalizability",
"Modalities - other",
"Organ - Heart",
"Organ - Lung",
"Visualisation in Biomedical Imaging"
] | [] | [] | null | null | Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has recently been demonstrated to allow the accurate estimation of patient age from chest... | 2207.01302 | title_snapshot |
Paper1796 | Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans | [
"Haomiao Ni",
"Yuan Xue",
"Kelvin Wong",
"John Volpi",
"Stephen T.C. Wong",
"James Z. Wang",
"Xiaolei Huang"
] | https://conferences.miccai.org/2022/papers/048-Paper1796.html | null | 10.1007/978-3-031-16452-1_40 | https://rdcu.be/cVVpV | null | [
"Image Segmentation",
"Modalities - CT",
"Organ - Brain"
] | [
"https://github.com/nihaomiao/MICCAI22_ADN"
] | [] | null | null | Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical practice, bilateral symmetric comparison of brain hemispheres is usually used to locate pathological abnormalities. Recent research has explored asymmetries to assis... | 2206.15445 | title_snapshot |
Paper2434 | Atlas-based Semantic Segmentation of Prostate Zones | [
"Jiazhen Zhang",
"Rajesh Venkataraman",
"Lawrence H. Staib",
"John A. Onofrey"
] | https://conferences.miccai.org/2022/papers/049-Paper2434.html | null | 10.1007/978-3-031-16443-9_55 | https://rdcu.be/cVRy9 | null | [
"Image Segmentation",
"Modalities - MRI"
] | [
"https://github.com/OnofreyLab/prostate_atlas_segm_miccai2022"
] | [] | null | null | Segmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integ... | null | null |
Paper0345 | Atlas-powered deep learning (ADL) - application to diffusion weighted MRI | [
"Davood Karimi",
"Ali Gholipour"
] | https://conferences.miccai.org/2022/papers/050-Paper0345.html | null | 10.1007/978-3-031-16431-6_12 | https://rdcu.be/cVD4T | null | [
"Organ - Brain",
"Modalities - MRI"
] | [] | [] | null | null | Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for bio... | 2205.03210 | title_snapshot |
Paper1798 | Attention mechanisms for physiological signal deep learning: which attention should we take? | [
"Seong-A Park",
"Hyung-Chul Lee",
"Chul-Woo Jung",
"Hyun-Lim Yang"
] | https://conferences.miccai.org/2022/papers/051-Paper1798.html | null | 10.1007/978-3-031-16431-6_58 | https://rdcu.be/cVD7e | null | [
"Machine Learning - Other",
"Modalities - EEG/ECG"
] | [] | [
"https://vitaldb.net"
] | null | null | Attention mechanisms are widely used to dramatically improve deep learning model performance in various fields. However, their general ability to improve the performance of physiological signal deep learning model is immature. In this study, we experimentally analyze four attention mechanisms (e.g., squeeze-and-excitat... | 2207.06904 | title_snapshot |
Paper2482 | Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation | [
"Md Sirajus Salekin",
"Ghada Zamzmi",
"Dmitry Goldgof",
"Peter R. Mouton",
"Kanwaljeet J. S. Anand",
"Terri Ashmeade",
"Stephanie Prescott",
"Yangxin Huang",
"Yu Sun"
] | https://conferences.miccai.org/2022/papers/052-Paper2482.html | null | 10.1007/978-3-031-16437-8_72 | https://rdcu.be/cVRuX | null | [
"Computer Aided Diagnosis",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Modalities - other",
"Modalities - Video",
"Outcome/disease prediction"
] | [] | [
"https://doi.org/10.1016/j.dib.2021.106796"
] | null | null | Artificial Intelligence (AI)-based methods allow for automatic assessment of pain intensity based on continuous monitoring and processing of subtle changes in sensory signals, including facial expression, body movements, and crying frequency. Currently, there is a large and growing need for expanding current AI-based a... | null | null |
Paper1971 | Attention-enhanced Disentangled Representation Learning for Unsupervised Domain Adaptation in Cardiac Segmentation | [
"Xiaoyi Sun",
"Zhizhe Liu",
"Shuai Zheng",
"Chen Lin",
"Zhenfeng Zhu",
"Yao Zhao"
] | https://conferences.miccai.org/2022/papers/053-Paper1971.html | null | 10.1007/978-3-031-16449-1_71 | https://rdcu.be/cVRXJ | null | [
"Machine Learning - Transfer learning",
"Image Segmentation",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Modalities - CT",
"Modalities - MRI",
"Organ - Heart"
] | [
"https://github.com/Sunxy11/ADR"
] | [] | null | null | To overcome the barriers of multimodality and scarcity of annotations in medical image segmentation, many unsupervised domain adaptation (UDA) methods have been proposed, especially in cardiac segmentation. However, these methods may not completely avoid the interference of domain-specific information. To tackle this p... | null | null |
Paper0676 | Attentive Symmetric Autoencoder for Brain MRI Segmentation | [
"Junjia Huang",
"Haofeng Li",
"Guanbin Li",
"Xiang Wan"
] | https://conferences.miccai.org/2022/papers/054-Paper0676.html | null | 10.1007/978-3-031-16443-9_20 | https://rdcu.be/cVRyy | null | [
"Computer Aided Diagnosis",
"Organ - Brain"
] | [] | [
"http://adni.loni.usc.edu/",
"https://www.oasis-brains.org/",
"http://www.braintumorsegmentation.org/",
"https://www.nitrc.org/projects/ibsr/",
"https://wmh.isi.uu.nl/data/"
] | null | null | Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding. However, existing methods seldom study the various importance of reconstructed patches and... | 2209.08887 | title_snapshot |
Paper0121 | Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging | [
"Ekaterina Kuzmina",
"Artem Razumov",
"Oleg Y. Rogov",
"Elfar Adalsteinsson",
"Jacob White",
"Dmitry V. Dylov"
] | https://conferences.miccai.org/2022/papers/055-Paper0121.html | null | 10.1007/978-3-031-16446-0_35 | https://rdcu.be/cVRTu | null | [
"Image Reconstruction",
"Image Registration",
"Modalities - MRI",
"Organ - Musculoskeleton"
] | [
"https://github.com/cviaai/AF-PLUS"
] | [] | null | null | Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove motion artifacts. The method takes the best of both worlds: the optimization-based ... | 2203.05569 | title_snapshot |
Paper0386 | AutoGAN-Synthesizer: Neural Architecture Search for Cross-Modality MRI Synthesis | [
"Xiaobin Hu",
"Ruolin Shen",
"Donghao Luo",
"Ying Tai",
"Chengjie Wang",
"Bjoern H. Menze"
] | https://conferences.miccai.org/2022/papers/056-Paper0386.html | null | 10.1007/978-3-031-16446-0_38 | https://rdcu.be/cVRTx | null | [
"Image Reconstruction",
"Modalities - MRI"
] | [] | [] | null | null | Considering the difficulty to obtain complete multi-modality MRI scans in some real-world data acquisition situations, synthesizing MRI data is a highly relevant and important topic to complement diagnosis information in clinical practice. In this study, we present a novel MRI synthesizer, called AutoGAN-Synthesizer, w... | null | null |
Paper1988 | AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy | [
"Ziyi Wang",
"Bo Lu",
"Yonghao Long",
"Fangxun Zhong",
"Tak-Hong Cheung",
"Qi Dou",
"Yunhui Liu"
] | https://conferences.miccai.org/2022/papers/057-Paper1988.html | null | 10.1007/978-3-031-16449-1_46 | https://rdcu.be/cVRXl | null | [
"Image Analysis in Robot-Assisted Surgery",
"Image-Guided Interventions",
"Modalities - Endoscope",
"Surgical Data Science",
"Surgical Scene Understanding",
"Surgical Skill and Work Flow Analysis"
] | [] | [
"https://autolaparo.github.io"
] | null | null | Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during t... | 2208.02049 | title_snapshot |
Paper1919 | Automated Classification of General Movements in Infants Using Two-stream Spatiotemporal Fusion Network | [
"Yuki Hashimoto",
"Akira Furui",
"Koji Shimatani",
"Maura Casadio",
"Paolo Moretti",
"Pietro Morasso",
"Toshio Tsuji"
] | https://conferences.miccai.org/2022/papers/058-Paper1919.html | null | 10.1007/978-3-031-16434-7_72 | https://rdcu.be/cVRsE | null | [
"Computer Aided Diagnosis",
"Image Segmentation",
"Machine Learning - Other",
"Modalities - Video"
] | [
"https://github.com/uoNuM/two-stream-gma"
] | [] | null | null | The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. However, its evaluation in clinical practice relies on visual inspection by experts, and an automated solution is eagerly awaited. Recently, video-based GMs classification has attracted attentio... | 2207.03344 | title_judge |
Paper2247 | Automatic Detection of Steatosis in Ultrasound Images with Comparative Visual Labeling | [
"Güinther Saibro",
"Michele Diana",
"Benoît Sauer",
"Jacques Marescaux",
"Alexandre Hostettler",
"Toby Collins"
] | https://conferences.miccai.org/2022/papers/059-Paper2247.html | null | 10.1007/978-3-031-16437-8_39 | https://rdcu.be/cVRtp | null | [
"Computer Aided Diagnosis",
"Machine Learning - Other",
"Modalities - Ultrasound",
"Organ - Abdomen"
] | [
"https://github.com/IRCAD/cvl"
] | [
"https://www.ircad.fr/research/data-sets/"
] | null | null | A common difficulty in computer-assisted diagnosis is acquiring accurate and representative labeled data, required to train, test and monitor models. Concerning liver steatosis detection in ultrasound (US) images, labeling images with human annotators can be error-prone because of subjectivity and decision boundary bia... | null | null |
Paper1361 | Automatic identification of segmentation errors for radiotherapy using geometric learning | [
"Edward G. A. Henderson",
"Andrew F. Green",
"Marcel van Herk",
"Eliana M. Vasquez Osorio"
] | https://conferences.miccai.org/2022/papers/060-Paper1361.html | null | 10.1007/978-3-031-16443-9_31 | https://rdcu.be/cVRyL | null | [
"Image Segmentation",
"Machine Learning - Data efficient Learning",
"Machine Learning - Other",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Machine Learning - Transfer learning",
"Modalities - CT",
"Organ - Other"
] | [
"https://github.com/rrr-uom-projects/contour_auto_QATool"
] | [
"https://github.com/deepmind/tcia-ct-scan-dataset"
] | null | null | Automatic segmentation of organs-at-risk (OARs) in CT scans using convolutional neural networks (CNNs) is being introduced into the radiotherapy workflow. However, these segmentations still require manual editing and approval by clinicians prior to clinical use, which can be time consuming. The aim of this work was to ... | 2206.13317 | title_snapshot |
Paper2252 | Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets | [
"Raja Ebsim",
"Benjamin G. Faber",
"Fiona Saunders",
"Monika Frysz",
"Jenny Gregory",
"Nicholas C. Harvey",
"Jonathan H. Tobias",
"Claudia Lindner",
"Timothy F. Cootes"
] | https://conferences.miccai.org/2022/papers/061-Paper2252.html | null | 10.1007/978-3-031-16443-9_1 | https://rdcu.be/cVRx7 | null | [
"Image Segmentation",
"Computer Aided Diagnosis",
"Machine Learning - Other",
"Organ - Musculoskeleton"
] | [] | [] | null | null | Osteophytes are distinctive radiographic features of osteoarthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, th... | null | null |
Paper0295 | Automating Blastocyst Formation and Quality Prediction in Time-Lapse Imaging with Adaptive Key Frame Selection | [
"Tingting Chen",
"Yi Cheng",
"Jinhong Wang",
"Zhaoxia Yang",
"Wenhao Zheng",
"Danny Z. Chen",
"Jian Wu"
] | https://conferences.miccai.org/2022/papers/062-Paper0295.html | null | 10.1007/978-3-031-16440-8_43 | https://rdcu.be/cVRwu | null | [
"Modalities - Video",
"Computer Aided Diagnosis"
] | [] | [] | null | null | Effective approaches for accurately predicting the developmental potential of embryos and selecting suitable embryos for blastocyst culture are critically needed. Many deep learning (DL) based methods for time-lapse monitoring (TLM) videos have been proposed to tackle this problem. Although fruitful, these methods are ... | null | null |
Paper2315 | Automation of clinical measurements on radiographs of children's hips | [
"Peter Thompson",
"Medical Annotation Collaborative",
"Daniel C. Perry",
"Timothy F. Cootes",
"Claudia Lindner"
] | https://conferences.miccai.org/2022/papers/063-Paper2315.html | null | 10.1007/978-3-031-16437-8_40 | https://rdcu.be/cVRtq | null | [
"Computer Aided Diagnosis",
"Computational Anatomy and Physiology",
"Organ - Musculoskeleton",
"Outcome/disease prediction"
] | [] | [] | null | null | Developmental dysplasia of the hip (DDH) and cerebral palsy (CP) related hip migration are two of the most common orthopaedic diseases in children, each affecting around 1-2 in 1000 children. For both of these conditions, early detection is a key factor in long term outcomes for patients. However, early signs of the di... | null | null |
Paper1731 | BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video | [
"Szymon Płotka",
"Michal K. Grzeszczyk",
"Robert Brawura-Biskupski-Samaha",
"Paweł Gutaj",
"Michał Lipa",
"Tomasz Trzciński",
"Arkadiusz Sitek"
] | https://conferences.miccai.org/2022/papers/064-Paper1731.html | null | 10.1007/978-3-031-16440-8_34 | https://rdcu.be/cVRvZ | null | [
"Computer Aided Diagnosis",
"Modalities - Ultrasound",
"Modalities - Video",
"Organ - Fetal",
"Outcome/disease prediction"
] | [
"https://github.com/SanoScience/BabyNet"
] | [] | null | null | Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of weight using prenatal ultrasound is challenging as it requires images of specific fetal body parts during adv... | 2205.09382 | title_snapshot |
Paper0280 | Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery | [
"Jingwei Song",
"Qiuchen Zhu",
"Jianyu Lin",
"Maani Ghaffari"
] | https://conferences.miccai.org/2022/papers/065-Paper0280.html | null | 10.1007/978-3-031-16449-1_32 | https://rdcu.be/cVRW7 | null | [
"Image-Guided Interventions",
"Image Registration",
"Interventional Imaging Systems",
"Interventional Simulation Systems"
] | [
"https://github.com/JingweiSong/BDIS.git"
] | [
"https://github.com/JingweiSong/BDIS.git"
] | null | null | This paper reports a CPU-level real-time stereo matching method for surgical images (10 Hz on 640*480 image with a single core of i5-9400). The proposed method is built on the fast LK algorithm, which estimates the disparity of the stereo images patch-wisely and in a coarse-to-fine manner. We propose a Bayesian framewo... | 2106.07136 | title_snapshot |
Paper2505 | Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation | [
"Mou-Cheng Xu",
"Yukun Zhou",
"Chen Jin",
"Marius de Groot",
"Daniel C. Alexander",
"Neil P. Oxtoby",
"Yipeng Hu",
"Joseph Jacob"
] | https://conferences.miccai.org/2022/papers/066-Paper2505.html | null | 10.1007/978-3-031-16443-9_56 | https://rdcu.be/cVRza | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Image Segmentation",
"Modalities - CT",
"Organ - Lung",
"Organ - Vessel"
] | [
"https://github.com/moucheng2017/EMSSL"
] | [
"https://arteryvein.grand-challenge.org/Home/",
"https://www.med.upenn.edu/sbia/brats2018/data.html"
] | null | null | This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) algorithm for clear statistical interpretation. Secondly, we propose a semi-supervised medical image segmentation method purely based on the o... | 2208.04435 | title_snapshot |
Paper1462 | Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology | [
"Yunlong Zhang",
"Yuxuan Sun",
"Honglin Li",
"Sunyi Zheng",
"Chenglu Zhu",
"Lin Yang"
] | https://conferences.miccai.org/2022/papers/067-Paper1462.html | null | 10.1007/978-3-031-16434-7_24 | https://rdcu.be/cVRrG | null | [
"Computational (Integrative) Pathology",
"Machine Learning - Model Generalizability"
] | [
"https://github.com/superjamessyx/robustness_benchmark"
] | [
"https://patchcamelyon.grand-challenge.org/"
] | null | null | When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted imag... | 2206.14973 | title_snapshot |
Paper2856 | BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis | [
"Masoud Monajatipoor",
"Mozhdeh Rouhsedaghat",
"Liunian Harold Li",
"C.-C. Jay Kuo",
"Aichi Chien",
"Kai-Wei Chang"
] | https://conferences.miccai.org/2022/papers/068-Paper2856.html | null | 10.1007/978-3-031-16443-9_69 | https://rdcu.be/cVRzn | null | [
"Computer Aided Diagnosis",
"Machine Learning - Other",
"Machine Learning - Transfer learning",
"Modalities - other",
"Modalities - Text (clinical/radiology reports)",
"Organ - Lung"
] | [
"https://github.com/monajati/BERTHop"
] | [
"https://openi.nlm.nih.gov/"
] | null | null | Vision-and-language (V&L) models take image and text as input and learn to capture the associations between them. These models can potentially deal with the tasks that involve understanding medical images along with their associated text. However, applying V&L models in the medical domain is challenging due to the expe... | 2108.04938 | title_snapshot |
Paper1459 | Bi-directional Encoding for Explicit Centerline Segmentation by Fully-Convolutional Networks | [
"Ilyas Sirazitdinov",
"Axel Saalbach",
"Heinrich Schulz",
"Dmitry V. Dylov"
] | https://conferences.miccai.org/2022/papers/069-Paper1459.html | null | 10.1007/978-3-031-16440-8_66 | https://rdcu.be/cVRwT | null | [
"Image Segmentation",
"Computer Aided Diagnosis",
"Image-Guided Interventions",
"Interventional Imaging Systems",
"Modalities - other",
"Organ - Heart",
"Organ - Lung",
"Organ - Vessel"
] | [] | [
"https://www.kaggle.com/competitions/ranzcr-clip-catheter-line-classification"
] | null | null | Localization of tube-shaped objects is an important topic in medical imaging. Previously it was mainly addressed via dense segmentation that may produce inconsistent results for long and narrow objects. In our work, we propose a point-based approach for explicit centerline segmentation that can be learned by fully-conv... | null | null |
Paper1108 | BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes | [
"Netanell Avisdris",
"Leo Joskowicz",
"Brian Dromey",
"Anna L. David",
"Donald M. Peebles",
"Danail Stoyanov",
"Dafna Ben Bashat",
"Sophia Bano"
] | https://conferences.miccai.org/2022/papers/070-Paper1108.html | null | 10.1007/978-3-031-16440-8_27 | https://rdcu.be/cVRvS | null | [
"Computer Aided Diagnosis",
"Machine Learning - Model Generalizability",
"Modalities - Ultrasound",
"Organ - Fetal"
] | [
"https://github.com/netanellavisdris/fetalbiometry"
] | [
"https://doi.org/10.5281/zenodo.3904280",
"https://zenodo.org/record/1327317"
] | null | null | Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be a time-consuming and oper... | 2206.14678 | title_snapshot |
Paper1852 | BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning | [
"Yi Gu",
"Yoshito Otake",
"Keisuke Uemura",
"Mazen Soufi",
"Masaki Takao",
"Nobuhiko Sugano",
"Yoshinobu Sato"
] | https://conferences.miccai.org/2022/papers/071-Paper1852.html | null | 10.1007/978-3-031-16446-0_61 | https://rdcu.be/cVRT5 | null | [
"Image Reconstruction",
"Computer Aided Diagnosis",
"Modalities - other",
"Organ - Musculoskeleton"
] | [
"https://github.com/NAIST-ICB/BMD-GAN"
] | [] | null | null | We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. Measuring BMD from an x-... | 2207.03210 | title_snapshot |
Paper0820 | Boundary-Enhanced Self-Supervised Learning for Brain Structure Segmentation | [
"Feng Chang",
"Chaoyi Wu",
"Yanfeng Wang",
"Ya Zhang",
"Xin Chen",
"Qi Tian"
] | https://conferences.miccai.org/2022/papers/072-Paper0820.html | null | 10.1007/978-3-031-16431-6_2 | https://rdcu.be/cVD4J | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Image Registration",
"Organ - Brain"
] | [
"https://github.com/changfeng3168/BE-SSL"
] | [] | null | null | To alleviate the demand for large amount of annotated data by deep learning methods, this paper explores self-supervised learning (SSL) for brain structure segmentation. Most SSL methods treat all pixels equally, failing to emphasize the boundaries that are important clues for segmentation. We propose Boundary-Enhanced... | null | null |
Paper0016 | BoxPolyp: Boost Generalized Polyp Segmentation using Extra Coarse Bounding Box Annotations | [
"Jun Wei",
"Yiwen Hu",
"Guanbin Li",
"Shuguang Cui",
"S. Kevin Zhou",
"Zhen Li"
] | https://conferences.miccai.org/2022/papers/073-Paper0016.html | null | 10.1007/978-3-031-16437-8_7 | https://rdcu.be/cVRsT | null | [
"Image Segmentation",
"Modalities - Endoscope"
] | [] | [] | null | null | Accurate polyp segmentation is of great importance for colorectal cancer diagnosis and treatment. However, due to the high cost of producing accurate mask annotations, existing polyp segmentation methods suffer from severe data shortage and impaired model generalization. Reversely, coarse polyp bounding box annotations... | 2212.03498 | title_snapshot |
Paper2706 | Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer’s Disease | [
"Mehmet Saygın Seyfioğlu",
"Zixuan Liu",
"Pranav Kamath",
"Sadjyot Gangolli",
"Sheng Wang",
"Thomas Grabowski",
"Linda Shapiro"
] | https://conferences.miccai.org/2022/papers/074-Paper2706.html | null | 10.1007/978-3-031-16431-6_44 | https://rdcu.be/cVD60 | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Machine Learning - Data efficient Learning",
"Machine Learning - Interpretability / Explainability",
"Modalities - MRI",
"Organ - Brain"
] | [
"https://github.com/aldraus/BrainAwareReplacementsForAD"
] | [
"https://ida.loni.usc.edu/login.jsp?project=ADNI&page=HOME"
] | null | null | We propose a novel framework for Alzheimer’s disease (AD) detection using brain MRIs. The framework starts with a data augmentation method called Brain-Aware Replacements (BAR), which leverages a standard brain parcellation to replace medically-relevant 3D brain regions in an anchor MRI from a randomly picked MRI to cr... | 2207.04574 | title_snapshot |
Paper2270 | Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training | [
"Constantin Seibold",
"Simon Reiß",
"M. Saquib Sarfraz",
"Rainer Stiefelhagen",
"Jens Kleesiek"
] | https://conferences.miccai.org/2022/papers/075-Paper2270.html | null | 10.1007/978-3-031-16443-9_66 | https://rdcu.be/cVRzk | null | [
"Modalities - Text (clinical/radiology reports)",
"Machine Learning - Model Generalizability",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Machine Learning - Transfer learning"
] | [] | [] | null | null | When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as ... | 2205.07139 | title_snapshot |
Paper1095 | Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection | [
"Florian Thamm",
"Oliver Taubmann",
"Markus Jürgens",
"Aleksandra Thamm",
"Felix Denzinger",
"Leonhard Rist",
"Hendrik Ditt",
"Andreas Maier"
] | https://conferences.miccai.org/2022/papers/076-Paper1095.html | null | 10.1007/978-3-031-16437-8_61 | https://rdcu.be/cVRuM | null | [
"Computer Aided Diagnosis",
"Modalities - CT",
"Organ - Brain"
] | [] | [] | null | null | Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, accurate and automated diagnosis of these scans is desirable. Human readers compare the left and right hemispheres in their assessment of strok... | 2205.02848 | title_snapshot |
Paper0239 | CACTUSS: Common Anatomical CT-US Space for US examinations | [
"Yordanka Velikova",
"Walter Simson",
"Mehrdad Salehi",
"Mohammad Farid Azampour",
"Philipp Paprottka",
"Nassir Navab"
] | https://conferences.miccai.org/2022/papers/077-Paper0239.html | null | 10.1007/978-3-031-16437-8_47 | https://rdcu.be/cVRuy | null | [
"Image-Guided Interventions",
"Image Segmentation",
"Machine Learning - Transfer learning",
"Modalities - CT",
"Organ - Abdomen",
"Organ - Vessel"
] | [
"https://github.com/danivelikova/cactuss"
] | [] | null | null | Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has been utilized for diagnostics, but due to its limited image quality and operator dependency, CT scans are usually required for monitoring and... | 2207.08619 | title_snapshot |
Paper0017 | Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation | [
"Yiqun Lin",
"Huifeng Yao",
"Zezhong Li",
"Guoyan Zheng",
"Xiaomeng Li"
] | https://conferences.miccai.org/2022/papers/078-Paper0017.html | null | 10.1007/978-3-031-16452-1_11 | https://rdcu.be/cVRYO | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Modalities - MRI",
"Organ - Musculoskeleton"
] | [
"https://github.com/xmed-lab/CLD-Semi"
] | [] | null | null | Segmentation of 3D knee MR images is important for the assessment of osteoarthritis. Like other medical data, the volume-wise labeling of knee MR images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL), particularly barely-supervised learning, is highly desirable for training with insuffic... | 2205.03644 | title_snapshot |
Paper0963 | Calibration of Medical Imaging Classification Systems with Weight Scaling | [
"Lior Frenkel",
"Jacob Goldberger"
] | https://conferences.miccai.org/2022/papers/079-Paper0963.html | null | 10.1007/978-3-031-16452-1_61 | https://rdcu.be/cVVqi | null | [
"Machine Learning - Other",
"Machine Learning - Interpretability / Explainability",
"Organ - Breast"
] | [] | [] | null | null | Calibrating neural networks is crucial in medical analysis applications where the decision making depends on the predicted probabilities. Modern neural networks are not well calibrated and they tend to overestimate probabilities when compared to the expected accuracy. This results in a misleading reliability that corru... | null | null |
Paper1145 | Camera Adaptation for Fundus-Image-Based CVD Risk Estimation | [
"Zhihong Lin",
"Danli Shi",
"Donghao Zhang",
"Xianwen Shang",
"Mingguang He",
"Zongyuan Ge"
] | https://conferences.miccai.org/2022/papers/080-Paper1145.html | null | 10.1007/978-3-031-16434-7_57 | https://rdcu.be/cVRsp | null | [
"Organ - Eye",
"Machine Learning - Model Generalizability",
"Outcome/disease prediction"
] | [
"https://github.com/linzhlalala/CVD-risk-based-on-retinal-fundus-images"
] | [] | null | null | Recent studies have validated the association between cardiovascular disease (CVD) risk and retinal fundus images. Combining deep learning (DL) and portable fundus cameras will enable CVD risk estimation in various scenarios and improve healthcare democratization. However, there are still significant issues to be solve... | 2206.09202 | title_snapshot |
Paper2332 | Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction | [
"Dominik J. E. Waibel",
"Scott Atwell",
"Matthias Meier",
"Carsten Marr",
"Bastian Rieck"
] | https://conferences.miccai.org/2022/papers/081-Paper2332.html | null | 10.1007/978-3-031-16440-8_15 | https://rdcu.be/cVRvF | null | [
"Image Reconstruction",
"Image Segmentation",
"Machine Learning - Other",
"Modalities - Microscopy"
] | [
"https://github.com/marrlab/SHAPR_torch"
] | [] | null | null | Reconstructing 3D objects from 2D images is both challenging for our brains and machine learning algorithms. To support this spatial reasoning task, contextual information about the overall shape of an object is critical. However, such information is not captured by established loss terms (e.g. Dice loss). We propose t... | 2203.01703 | title_snapshot |
Paper2156 | Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis | [
"Raghavendra Selvan",
"Nikhil Bhagwat",
"Lasse F. Wolff Anthony",
"Benjamin Kanding",
"Erik B. Dam"
] | https://conferences.miccai.org/2022/papers/082-Paper2156.html | null | 10.1007/978-3-031-16443-9_49 | https://rdcu.be/cVRy3 | null | [
"Machine Learning - Other",
"Image Segmentation",
"Machine Learning - Data efficient Learning"
] | [] | [] | null | null | The increasing energy consumption and carbon footprint of deep learning (DL) due to growing compute requirements has become a cause of concern. In this work, we focus on the carbon footprint of developing DL models for medical image analysis (MIA), where volumetric images of high spatial resolution are handled. In this... | 2203.02202 | title_snapshot |
Paper0783 | CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data | [
"Hao Ding",
"Jintan Zhang",
"Peter Kazanzides",
"Jie Ying Wu",
"Mathias Unberath"
] | https://conferences.miccai.org/2022/papers/083-Paper0783.html | null | 10.1007/978-3-031-16449-1_37 | https://rdcu.be/cVRXc | null | [
"Image Analysis in Robot-Assisted Surgery",
"Image Segmentation"
] | [
"https://github.com/hding2455/CaRTS"
] | [] | null | null | Vision-based segmentation of the robotic tool during robot-assisted surgery enables downstream applications, such as augmented reality feedback, while allowing for inaccuracies in robot kinematics. With the introduction of deep learning, many methods were presented to solve instrument segmentation directly and solely f... | 2203.09475 | title_snapshot |
Paper1369 | CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis | [
"Ignacio Sarasua",
"Sebastian Pölsterl",
"Christian Wachinger"
] | https://conferences.miccai.org/2022/papers/084-Paper1369.html | null | 10.1007/978-3-031-16431-6_5 | https://rdcu.be/cVD4M | null | [
"Machine Learning - Other",
"Computer Aided Diagnosis",
"Machine Learning - Model Generalizability",
"Machine Learning - Transfer learning",
"Modalities - MRI",
"Organ - Brain"
] | [
"https://github.com/ai-med"
] | [] | null | null | Modeling temporal changes in subcortical structures is crucial for a better understanding of the progression of Alzheimer’s disease (AD). Given their flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer architectures have been proposed in the past for predicting hippocampus deformations across... | 2207.02091 | title_snapshot |
Paper0894 | Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images | [
"Renato Hermoza",
"Gabriel Maicas",
"Jacinto C. Nascimento",
"Gustavo Carneiro"
] | https://conferences.miccai.org/2022/papers/085-Paper0894.html | null | 10.1007/978-3-031-16449-1_21 | https://rdcu.be/cVRU1 | null | [
"Outcome/disease prediction",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Modalities - CT",
"Modalities - Histopathology"
] | [
"https://github.com/renato145/CASurv"
] | [] | null | null | Survival time prediction from medical images is important for treatment planning, where accurate estimations can improve healthcare quality. One issue affecting the training of survival models is censored data. Most of the current survival prediction approaches are based on Cox models that can deal with censored data, ... | 2205.13226 | title_snapshot |
Paper0497 | CephalFormer: Incorporating Global Structure Constraint into Visual Features for General Cephalometric Landmark Detection | [
"Yankai Jiang",
"Yiming Li",
"Xinyue Wang",
"Yubo Tao",
"Jun Lin",
"Hai Lin"
] | https://conferences.miccai.org/2022/papers/086-Paper0497.html | null | 10.1007/978-3-031-16437-8_22 | https://rdcu.be/cVRs8 | null | [
"Computer Aided Diagnosis",
"Modalities - CT",
"Organ - Musculoskeleton"
] | [] | [] | null | null | Accurate cephalometric landmark detection is a crucial step in orthodontic diagnosis and therapy planning. However, existing deep learning-based methods lack the ability to explicitly model the complex dependencies among visual features and landmarks. Therefore, they fail to adaptively encode the landmark’s global stru... | null | null |
Paper2197 | Cerebral Microbleeds Detection Using a 3D Feature Fused Region Proposal Network with Hard Sample Prototype Learning | [
"Jun-Ho Kim",
"Mohammed A. Al-masni",
"Seul Lee",
"Haejoon Lee",
"Dong-Hyun Kim"
] | https://conferences.miccai.org/2022/papers/087-Paper2197.html | null | 10.1007/978-3-031-16431-6_43 | https://rdcu.be/cVD6Z | null | [
"Computer Aided Diagnosis",
"Machine Learning - Other",
"Modalities - MRI",
"Organ - Brain",
"Organ - Vessel",
"Outcome/disease prediction"
] | [] | [] | null | null | Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to cerebrovascular diseases, including cognitive decline, intracerebral hemorrhage, and cerebral infarction. However, manual detection of the CMBs is a time-consuming and error-prone process be-ca... | null | null |
Paper0402 | CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs | [
"Minghui Zhang",
"Hanxiao Zhang",
"Guang-Zhong Yang",
"Yun Gu"
] | https://conferences.miccai.org/2022/papers/088-Paper0402.html | null | 10.1007/978-3-031-16431-6_48 | https://rdcu.be/cVD64 | null | [
"Machine Learning - Transfer learning",
"Modalities - CT",
"Organ - Lung"
] | [
"https://github.com/Puzzled-Hui/CFDA"
] | [
"http://www.pami.sjtu.edu.cn/Show/56/126"
] | null | null | Detailed modeling of the airway tree from CT scan is important for 3D navigation involved in endobronchial intervention including for those patients infected with the novel coronavirus. Deep learning methods have the potential for automatic airway segmentation but require large annotated datasets for training, which is... | null | null |
Paper1601 | Characterization of brain activity patterns across states of consciousness based on variational auto-encoders | [
"Chloé Gomez",
"Antoine Grigis",
"Lynn Uhrig",
"Béchir Jarraya"
] | https://conferences.miccai.org/2022/papers/089-Paper1601.html | null | 10.1007/978-3-031-16431-6_40 | https://rdcu.be/cVD6W | null | [
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Computer Aided Diagnosis",
"Modalities - MRI",
"Organ - Brain",
"Visualisation in Biomedical Imaging"
] | [] | [] | null | null | Decoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. The spontaneous fluctuations of brain activity through different patterns across time are monitored using resting-state functional MRI. The different dynamic functional configurations of the brain during resting-... | null | null |
Paper1593 | CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays | [
"Gaurang Karwande",
"Amarachi B. Mbakwe",
"Joy T. Wu",
"Leo A. Celi",
"Mehdi Moradi",
"Ismini Lourentzou"
] | https://conferences.miccai.org/2022/papers/090-Paper1593.html | null | 10.1007/978-3-031-16431-6_55 | https://rdcu.be/cVD7b | null | [
"Computer Aided Diagnosis",
"Machine Learning - Other",
"Outcome/disease prediction"
] | [
"https://github.com/PLAN-Lab/ChexRelNet"
] | [
"https://physionet.org/content/mimic-cxr-jpg/2.0.0/",
"https://physionet.org/content/chest-imagenome/1.0.0/"
] | null | null | Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention. Monitoring the progression of pathologies that are visualized through chest imaging poses several challenges in anatomica... | 2208.03873 | title_snapshot |
Paper1588 | ChrSNet: Chromosome Straightening using Self-attention Guided Networks | [
"Sunyi Zheng",
"Jingxiong Li",
"Zhongyi Shui",
"Chenglu Zhu",
"Yunlong Zhang",
"Pingyi Chen",
"Lin Yang"
] | https://conferences.miccai.org/2022/papers/091-Paper1588.html | null | 10.1007/978-3-031-16440-8_12 | https://rdcu.be/cVRvw | null | [
"Modalities - Microscopy",
"Computer Aided Diagnosis",
"Machine Learning - Other"
] | [
"https://github.com/lijx1996/ChrSNet"
] | [] | null | null | Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attent... | 2207.00147 | title_snapshot |
Paper2297 | CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction | [
"Wookjin Choi",
"Navdeep Dahiya",
"Saad Nadeem"
] | https://conferences.miccai.org/2022/papers/092-Paper2297.html | null | 10.1007/978-3-031-16443-9_2 | https://rdcu.be/cVRx8 | null | [
"Computational Anatomy and Physiology",
"Image Segmentation",
"Modalities - CT",
"Organ - Abdomen",
"Visualisation in Biomedical Imaging"
] | [
"https://github.com/nadeemlab/CIR"
] | [
"https://zenodo.org/record/6762573"
] | null | null | Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment... | 2206.14903 | title_snapshot |
Paper1320 | Class Impression for Data-free Incremental Learning | [
"Sana Ayromlou",
"Purang Abolmaesumi",
"Teresa Tsang",
"Xiaoxiao Li"
] | https://conferences.miccai.org/2022/papers/093-Paper1320.html | null | 10.1007/978-3-031-16440-8_31 | https://rdcu.be/cVRvW | null | [
"Machine Learning - Continual Learning",
"Machine Learning - Model Generalizability",
"Machine Learning - Other",
"Modalities - Ultrasound",
"Organ - Heart"
] | [
"https://github.com/sanaAyrml/Class-Impresion-for-Data-free-Incremental-Learning.git"
] | [] | null | null | Standard deep learning-based classification approaches require collecting all samples from all classes in advance and are trained offline. This paradigm may not be practical in real-world clinical applications, where new classes are incrementally introduced through the addition of new data. Class incremental learning i... | 2207.00005 | title_snapshot |
Paper1363 | Classification-aided High-quality PET Image Synthesis via Bidirectional Contrastive GAN with Shared Information Maximization | [
"Yuchen Fei",
"Chen Zu",
"Zhengyang Jiao",
"Xi Wu",
"Jiliu Zhou",
"Dinggang Shen",
"Yan Wang"
] | https://conferences.miccai.org/2022/papers/094-Paper1363.html | null | 10.1007/978-3-031-16446-0_50 | https://rdcu.be/cVRTJ | null | [
"Image Reconstruction",
"Modalities - PET/SPECT",
"Organ - Brain"
] | [] | [] | null | null | Positron emission tomography (PET) is a pervasively adopted nuclear imaging technique, however, its inherent tracer radiation inevitably causes potential health hazards to patients. To obtain high-quality PET image while reducing radiation exposure, this paper proposes an algorithm for high-quality standard-dose PET (S... | null | null |
Paper0431 | Clinical-realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worst-case Study | [
"Ziyue Xu",
"Andriy Myronenko",
"Dong Yang",
"Holger R. Roth",
"Can Zhao",
"Xiaosong Wang",
"Daguang Xu"
] | https://conferences.miccai.org/2022/papers/095-Paper0431.html | null | 10.1007/978-3-031-16434-7_8 | https://rdcu.be/cVRq6 | null | [
"Machine Learning - Data efficient Learning",
"Computer Aided Diagnosis",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning",
"Machine Learning - Uncertainty",
"Modalities - Histopathology"
] | [] | [
"https://camelyon16.grand-challenge.org/Data/"
] | null | null | Acquiring pixel-level annotation has been a major challenge for machine learning methods in medical image analysis. Such difficulty mainly comes from two sources: localization requiring high expertise, and delineation requiring tedious and time-consuming work. Existing methods of easing the annotation effort mostly foc... | null | null |
Paper2314 | CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy | [
"Shawn Mathew",
"Saad Nadeem",
"Arie Kaufman"
] | https://conferences.miccai.org/2022/papers/096-Paper2314.html | null | 10.1007/978-3-031-16449-1_49 | https://rdcu.be/cVRXo | null | [
"Surgical Data Science",
"Modalities - Endoscope",
"Modalities - Video",
"Organ - Abdomen",
"Visualisation in Biomedical Imaging"
] | [
"https://github.com/nadeemlab/CEP"
] | [] | null | null | Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with... | 2206.14951 | title_snapshot |
Paper0726 | Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration | [
"Ziyi Shen",
"Qianye Yang",
"Yuming Shen",
"Francesco Giganti",
"Vasilis Stavrinides",
"Richard Fan",
"Caroline Moore",
"Mirabela Rusu",
"Geoffrey Sonn",
"Philip Torr",
"Dean Barratt",
"Yipeng Hu"
] | https://conferences.miccai.org/2022/papers/097-Paper0726.html | null | 10.1007/978-3-031-16446-0_23 | https://rdcu.be/cVRS4 | null | [
"Image Registration",
"Machine Learning - Model Generalizability",
"Modalities - MRI",
"Organ - Other"
] | [] | [] | null | null | Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients. This paper describes a development in improving the learning-based registration algorithms, for this challenging clinical application often with highly variable yet limited training data. First, w... | 2207.06189 | title_snapshot |
Paper0779 | Combining mixed-format labels for AI-based pathology detection pipeline in a large-scale knee MRI study | [
"Micha Kornreich",
"JinHyeong Park",
"Joschka Braun",
"Jayashri Pawar",
"James Browning",
"Richard Herzog",
"Benjamin Odry",
"Li Zhang"
] | https://conferences.miccai.org/2022/papers/098-Paper0779.html | null | 10.1007/978-3-031-16452-1_18 | https://rdcu.be/cVRY0 | null | [
"Organ - Musculoskeleton",
"Computational (Integrative) Pathology",
"Computer Aided Diagnosis",
"Modalities - MRI",
"Outcome/disease prediction"
] | [] | [] | null | null | Labeling for pathology detection is a laborious task, performed by highly trained and expensive experts. Datasets often have mixed formats, including a mix of pathology positional labels and categorical labels. Successfully combining mixed-format data from multiple institutions for model training and evaluation is crit... | null | null |
Paper2128 | Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport | [
"Javid Dadashkarimi",
"Amin Karbasi",
"Dustin Scheinost"
] | https://conferences.miccai.org/2022/papers/099-Paper2128.html | null | 10.1007/978-3-031-16431-6_37 | https://rdcu.be/cVD6T | null | [
"Machine Learning - Model Generalizability",
"Visualisation in Biomedical Imaging"
] | [
"https://github.com/dadashkarimi/carot"
] | [
"https://www.humanconnectome.org/study/hcp-young-adult/document/900-subjects-data-release"
] | null | null | Connectomics is a popular approach for understanding the brain with neuroimaging data. Yet, a connectome generated from one atlas is different in size, topology, and scale compared to a connectome generated from another atlas. These differences hinder interpreting, generalizing, and combining connectomes and downstream... | null | null |
Paper1882 | Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance | [
"Chengwei Pan",
"Gangming Zhao",
"Junjie Fang",
"Baolian Qi",
"Jiaheng Liu",
"Chaowei Fang",
"Dingwen Zhang",
"Jinpeng Li",
"Yizhou Yu"
] | https://conferences.miccai.org/2022/papers/100-Paper1882.html | null | 10.1007/978-3-031-16431-6_59 | https://rdcu.be/cVD7f | null | [
"Computer Aided Diagnosis",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning"
] | [
"https://github.com/GangmingZhao/tb-attribute-weak-localization."
] | [
"https://github.com/GangmingZhao/tb-attribute-weak-localization"
] | null | null | Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption. Weakly supervised learning (WSL), which leverages coarse-grained labels to accomplish fine-grained tasks, ... | 2207.00251 | title_snapshot |
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