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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Linwei Wang

    1. Rochester Institute of Technology, Rochester, USA

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  2. Qi Dou

    1. Chinese University of Hong Kong, Hong Kong, Hong Kong

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  3. P. Thomas Fletcher

    1. University of Virginia, Charlottesville, USA

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  4. Stefanie Speidel

    1. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

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  5. Shuo Li

    1. Case Western Reserve University, Cleveland, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13438)

Included in the following conference series:

Conference proceedings info: MICCAI 2022.

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About this book

The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.
The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:

Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;

Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;

Part III: Breast imaging; colonoscopy; computer aided diagnosis;

Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;

Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;

Part VI: Image registration; image reconstruction;

Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization;

Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.

 




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Table of contents (70 papers)

  1. Front Matter

    Pages i-xli
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  2. Machine Learning – Weakly-Supervised Learning

    1. Front Matter

      Pages 1-1
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    2. CS\(^2\): A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention

      • Xiaodan Xing, Jiahao Huang, Yang Nan, Yinzhe Wu, Chengjia Wang, Zhifan Gao et al.
      Pages 3-12

    3. Stabilize, Decompose, and Denoise: Self-supervised Fluoroscopy Denoising

      • Ruizhou Liu, Qiang Ma, Zhiwei Cheng, Yuanyuan Lyu, Jianji Wang, S. Kevin Zhou
      Pages 13-23

    4. Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images

      • Fan Bai, Xiaohan Xing, Yutian Shen, Han Ma, Max Q.-H. Meng
      Pages 24-34

    5. Diffusion Models for Medical Anomaly Detection

      • Julia Wolleb, Florentin Bieder, Robin Sandkühler, Philippe C. Cattin
      Pages 35-45

    6. Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning

      • Xiajun Jiang, Zhiyuan Li, Ryan Missel, Md Shakil Zaman, Brian Zenger, Wilson W. Good et al.
      Pages 46-56

    7. Aggregative Self-supervised Feature Learning from Limited Medical Images

      • Jiuwen Zhu, Yuexiang Li, Lian Ding, S. Kevin Zhou
      Pages 57-66

    8. Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

      • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu
      Pages 67-76

    9. Adversarially Robust Prototypical Few-Shot Segmentation with Neural-ODEs

      • Prashant Pandey, Aleti Vardhan, Mustafa Chasmai, Tanuj Sur, Brejesh Lall
      Pages 77-87

    10. Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification

      • Zhiyuan Cai, Li Lin, Huaqing He, Xiaoying Tang
      Pages 88-98

    11. Self-supervised Learning of Morphological Representation for 3D EM Segments with Cluster-Instance Correlations

      • Chi Zhang, Qihua Chen, Xuejin Chen
      Pages 99-108

    12. Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

      • Yiqun Lin, Huifeng Yao, Zezhong Li, Guoyan Zheng, Xiaomeng Li
      Pages 109-118

    13. Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization

      • Qiushi Yang, Xinyu Liu, Zhen Chen, Bulat Ibragimov, Yixuan Yuan
      Pages 119-129

    14. GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation

      • Mark Endo, Kathleen L. Poston, Edith V. Sullivan, Li Fei-Fei, Kilian M. Pohl, Ehsan Adeli
      Pages 130-139

    15. Semi-supervised Medical Image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints

      • Jinhua Liu, Christian Desrosiers, Yuanfeng Zhou
      Pages 140-150

    16. MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-Ray Images of Multiple Body Parts

      • Weibin Liao, Haoyi Xiong, Qingzhong Wang, Yan Mo, Xuhong Li, Yi Liu et al.
      Pages 151-161

    17. ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation

      • Ke Zhang, Xiahai Zhuang
      Pages 162-172

    18. ProCo: Prototype-Aware Contrastive Learning for Long-Tailed Medical Image Classification

      • Zhixiong Yang, Junwen Pan, Yanzhan Yang, Xiaozhou Shi, Hong-Yu Zhou, Zhicheng Zhang et al.
      Pages 173-182

    19. 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 et al.
      Pages 183-192
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  8. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  9. Computational Biomechanics for Medicine

  10. Computational Biomechanics for Medicine

Editors and Affiliations

  • Rochester Institute of Technology, Rochester, USA

    Linwei Wang

  • Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  • University of Virginia, Charlottesville, USA

    P. Thomas Fletcher

  • National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  • Case Western Reserve University, Cleveland, USA

    Shuo Li

Bibliographic Information

  • Book Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  • Book Subtitle: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII

  • Editors: Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-16452-1

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-031-16451-4Published: 16 September 2022

  • eBook ISBN: 978-3-031-16452-1Published: 15 September 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XLI, 740

  • Number of Illustrations: 126 b/w illustrations, 210 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Signal, Image and Speech Processing

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