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ORCID25th MICCAI 2022: Singapore - Part VIII
- Linwei Wang, Qi Dou
, P. Thomas Fletcher, Stefanie Speidel, Shuo Li:
Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part VIII. Lecture Notes in Computer Science 13438, Springer 2022, ISBN 978-3-031-16451-4
Machine Learning - Weakly-Supervised Learning
- Xiaodan Xing, Jiahao Huang, Yang Nan, Yinzhe Wu, Chengjia Wang, Zhifan Gao, Simon Walsh, Guang Yang:
CS2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention. 3-12 - Ruizhou Liu, Qiang Ma, Zhiwei Cheng, Yuanyuan Lyu, Jianji Wang, S. Kevin Zhou:
Stabilize, Decompose, and Denoise: Self-supervised Fluoroscopy Denoising. 13-23 - Fan Bai, Xiaohan Xing, Yantao Shen, Han Ma, Max Q.-H. Meng:
Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images. 24-34 - Julia Wolleb, Florentin Bieder, Robin Sandkühler, Philippe C. Cattin:
Diffusion Models for Medical Anomaly Detection. 35-45 - Xiajun Jiang, Zhiyuan Li, Ryan Missel, Md Shakil Zaman, Brian Zenger, Wilson W. Good, Rob S. MacLeod, John L. Sapp, Linwei Wang:
Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning. 46-56 - Jiuwen Zhu, Yuexiang Li, Lian Ding, S. Kevin Zhou:
Aggregative Self-supervised Feature Learning from Limited Medical Images. 57-66 - Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu:
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images. 67-76 - Prashant Pandey, Aleti Vardhan, Mustafa Chasmai, Tanuj Sur, Brejesh Lall:
Adversarially Robust Prototypical Few-Shot Segmentation with Neural-ODEs. 77-87 - Zhiyuan Cai, Li Lin, Huaqing He, Xiaoying Tang:
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification. 88-98 - Chi Zhang, Qihua Chen, Xuejin Chen:
Self-supervised Learning of Morphological Representation for 3D EM Segments with Cluster-Instance Correlations. 99-108 - Yiqun Lin, Huifeng Yao, Zezhong Li, Guoyan Zheng, Xiaomeng Li:
Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation. 109-118 - Qiushi Yang, Xinyu Liu, Zhen Chen, Bulat Ibragimov, Yixuan Yuan:
Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization. 119-129 - Mark Endo, Kathleen L. Poston, Edith V. Sullivan, Li Fei-Fei, Kilian M. Pohl, Ehsan Adeli-Mosabbeb:
GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation. 130-139 - Jinhua Liu, Christian Desrosiers, Yuanfeng Zhou:
Semi-supervised Medical Image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints. 140-150 - Weibin Liao, Haoyi Xiong, Qingzhong Wang, Yan Mo, Xuhong Li, Yi Liu, Zeyu Chen, Siyu Huang, Dejing Dou:
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-Ray Images of Multiple Body Parts. 151-161 - Ke Zhang, Xiahai Zhuang:
ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation. 162-172 - Zhixiong Yang, Junwen Pan, Yanzhan Yang, Xiaozhou Shi, Hong-Yu Zhou, Zhicheng Zhang, Cheng Bian:
ProCo: Prototype-Aware Contrastive Learning for Long-Tailed Medical Image Classification. 173-182 - Micha Kornreich, JinHyeong Park, Joschka Braun, Jayashri Pawar, James Browning, Richard Herzog, Benjamin Odry, Li Zhang:
Combining Mixed-Format Labels for AI-Based Pathology Detection Pipeline in a Large-Scale Knee MRI Study. 183-192 - Yaojia Zheng, Zhouwu Liu, Rong Mo, Ziyi Chen, Wei-Shi Zheng, Ruixuan Wang:
Task-Oriented Self-supervised Learning for Anomaly Detection in Electroencephalography. 193-203 - Hao Bian, Zhuchen Shao, Yang Chen, Yifeng Wang, Haoqian Wang, Jian Zhang, Yongbing Zhang:
Multiple Instance Learning with Mixed Supervision in Gleason Grading. 204-213 - He Li, Yutaro Iwamoto, Xianhua Han, Lanfen Lin, Hongjie Hu, Yen-Wei Chen:
An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions. 214-223 - Hritam Basak, Sagnik Ghosal, Ram Sarkar:
Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI. 224-233 - Qiuhui Chen, Yi Hong:
Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations. 234-243 - Yixuan Wu, Bo Zheng, Jintai Chen, Danny Z. Chen, Jian Wu:
Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D Medical Image Segmentation. 244-254 - Xiaowei Yu, Dan Hu, Lu Zhang, Ying Huang, Zhengwang Wu, Tianming Liu, Li Wang, Weili Lin, Dajiang Zhu, Gang Li:
Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network. 255-264 - Sukesh Adiga V, Jose Dolz, Herve Lombaert:
Leveraging Labeling Representations in Uncertainty-Based Semi-supervised Segmentation. 265-275 - William Consagra, Martin Cole, Zhengwu Zhang:
Analyzing Brain Structural Connectivity as Continuous Random Functions. 276-285 - Jiawei Liu, Fuyong Xing, Abbas Shaikh, Marius George Linguraru, Antonio R. Porras:
Learning with Context Encoding for Single-Stage Cranial Bone Labeling and Landmark Localization. 286-296 - Vishwesh Nath, Dong Yang, Holger R. Roth, Daguang Xu:
Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning. 297-308 - Xinyu Liu, Wuyang Li, Yixuan Yuan:
Intervention & Interaction Federated Abnormality Detection with Noisy Clients. 309-319 - Botond Fazekas, Guilherme Aresta, Dmitry A. Lachinov, Sophie Riedl, Julia Mai, Ursula Schmidt-Erfurth, Hrvoje Bogunovic:
SD-LayerNet: Semi-supervised Retinal Layer Segmentation in OCT Using Disentangled Representation with Anatomical Priors. 320-329 - Martin J. Menten, Johannes C. Paetzold, Alina Dima, Bjoern H. Menze, Benjamin Knier, Daniel Rueckert:
Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs. 330-340 - Salome Kazeminia, Ario Sadafi, Asya Makhro, Anna Bogdanova, Shadi Albarqouni, Carsten Marr:
Anomaly-Aware Multiple Instance Learning for Rare Anemia Disorder Classification. 341-350 - Alvaro Gomariz, Huanxiang Lu, Yun Yvonna Li, Thomas Albrecht, Andreas Maunz, Fethallah Benmansour, Alessandra M. Valcarcel, Jennifer Luu, Daniela Ferrara, Orcun Goksel:
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation. 351-361
Machine Learning - Model Interpretation
- Tingting Dan, Hongmin Cai, Zhuobin Huang, Paul J. Laurienti, Won Hwa Kim, Guorong Wu:
Neuro-RDM: An Explainable Neural Network Landscape of Reaction-Diffusion Model for Cognitive Task Recognition. 365-374 - Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang:
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis. 375-385 - Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman:
Consistency-Preserving Visual Question Answering in Medical Imaging. 386-395 - Zhongyu Huang, Changde Du, Yingheng Wang, Huiguang He:
Graph Emotion Decoding from Visually Evoked Neural Responses. 396-405 - Tingsong Xiao, Lu Zeng, Xiaoshuang Shi, Xiaofeng Zhu, Guorong Wu:
Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer's Disease Diagnosis. 406-415 - Haomiao Ni, Yuan Xue, Kelvin K. Wong, John Volpi, Stephen T. C. Wong, James Z. Wang, Xiaolei Huang:
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-contrast CT Scans. 416-426 - Hanqing Chao, Jiajin Zhang, Pingkun Yan:
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images. 427-436 - Agnieszka Mikolajczyk, Sylwia Majchrowska, Sandra Carrasco Limeros:
The (de)biasing Effect of GAN-Based Augmentation Methods on Skin Lesion Images. 437-447 - Chiara Mauri, Stefano Cerri, Oula Puonti, Mark Mühlau, Koen Van Leemput:
Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model. 448-458 - Maryam Toloubidokhti, Nilesh Kumar, Zhiyuan Li, Prashnna K. Gyawali, Brian Zenger, Wilson W. Good, Rob S. MacLeod, Linwei Wang:
Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators. 459-468 - Houliang Zhou, Yu Zhang, Brian Y. Chen, Li Shen, Lifang He:
Sparse Interpretation of Graph Convolutional Networks for Multi-modal Diagnosis of Alzheimer's Disease. 469-478
Machine Learning - Uncertainty
- Jinyi Xiang, Peng Qiu, Yang Yang:
FUSSNet: Fusing Two Sources of Uncertainty for Semi-supervised Medical Image Segmentation. 481-491 - Thierry Judge, Olivier Bernard, Mihaela Porumb, Agisilaos Chartsias, Arian Beqiri, Pierre-Marc Jodoin:
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation. 492-502 - Ke Zou, Xuedong Yuan, Xiaojing Shen, Meng Wang, Huazhu Fu:
TBraTS: Trusted Brain Tumor Segmentation. 503-513 - Kaisar Kushibar, Víctor M. Campello, Lidia Garrucho, Akis Linardos, Petia Radeva, Karim Lekadir:
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation. 514-524 - Mohammad Mahdi Kazemi Esfeh, Zahra Gholami, Christina Luong, Teresa Tsang, Purang Abolmaesumi:
DEUE: Delta Ensemble Uncertainty Estimation for a More Robust Estimation of Ejection Fraction. 525-534 - Yidong Zhao, Changchun Yang, Artur M. Schweidtmann, Qian Tao:
Efficient Bayesian Uncertainty Estimation for nnU-Net. 535-544 - Charles Lu, Anastasios N. Angelopoulos, Stuart R. Pomerantz:
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets. 545-554
Machine Learning Theory and Methodologies
- Calvin-Khang Ta, Abhishek Aich, Akash Gupta, Amit K. Roy-Chowdhury:
Poisson2Sparse: Self-supervised Poisson Denoising from a Single Image. 557-567 - Lin Wang, Munan Ning, Donghuan Lu, Dong Wei, Yefeng Zheng, Jie Chen:
An Inclusive Task-Aware Framework for Radiology Report Generation. 568-577 - Samira Zare, Hien Van Nguyen:
Removal of Confounders via Invariant Risk Minimization for Medical Diagnosis. 578-587 - Jun Li, Shibo Li, Ying Hu, Huiren Tao:
A Self-guided Framework for Radiology Report Generation. 588-598 - Hadrien Reynaud, Athanasios Vlontzos, Mischa Dombrowski, Ciarán M. Gilligan-Lee, Arian Beqiri, Paul Leeson, Bernhard Kainz:
D'ARTAGNAN: Counterfactual Video Generation. 599-609 - Ming Kong, Zhengxing Huang, Kun Kuang, Qiang Zhu, Fei Wu:
TranSQ: Transformer-Based Semantic Query for Medical Report Generation. 610-620 - Luyang Luo, Dunyuan Xu, Hao Chen, Tien-Tsin Wong, Pheng-Ann Heng:
Pseudo Bias-Balanced Learning for Debiased Chest X-Ray Classification. 621-631 - Ruolin Su, Xiao Liu, Sotirios A. Tsaftaris:
Why Patient Data Cannot Be Easily Forgotten? 632-641 - Lior Frenkel, Jacob Goldberger:
Calibration of Medical Imaging Classification Systems with Weight Scaling. 642-651 - Yuhao Huang, Xin Yang, Xiaoqiong Huang, Jiamin Liang, Xinrui Zhou, Cheng Chen, Haoran Dou, Xindi Hu, Yan Cao, Dong Ni:
Online Reflective Learning for Robust Medical Image Segmentation. 652-662 - Chaoyu Chen, Xin Yang, Ruobing Huang, Xindi Hu, Yankai Huang, Xiduo Lu, Xinrui Zhou, Mingyuan Luo, Yinyu Ye, Xue Shuang, Juzheng Miao, Yi Xiong, Dong Ni:
Fine-Grained Correlation Loss for Regression. 663-672 - Naif Alkhunaizi, Dmitry Kamzolov, Martin Takác, Karthik Nandakumar:
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. 673-683 - Nicholas Konz, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski:
The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning. 684-694 - Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete:
FedHarmony: Unlearning Scanner Bias with Distributed Data. 695-704 - Walter H. L. Pinaya, Mark S. Graham, Robert J. Gray, Pedro F. Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee-Haur Mah, Andrew D. MacKinnon, James T. Teo, Hans Rolf Jäger, David Werring, Geraint Rees, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso:
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models. 705-714 - Rudy Rizzo, Martyna Dziadosz, Sreenath P. Kyathanahally, Mauricio Reyes, Roland Kreis:
Reliability of Quantification Estimates in MR Spectroscopy: CNNs vs Traditional Model Fitting. 715-724 - Khanh Nguyen, Huy Hoang Nguyen, Aleksei Tiulpin:
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching. 725-735
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