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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

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

  • 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 13433)

Included in the following conference series:

Conference proceedings info: MICCAI 2022.

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Table of contents (75 papers)

  1. Front Matter

    Pages i-xli
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  2. Breast Imaging

    1. Front Matter

      Pages 1-1
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    2. Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation

      • Yuanhong Chen, Hu Wang, Chong Wang, Yu Tian, Fengbei Liu, Yuyuan Liu et al.
      Pages 3-13

    3. Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models

      • Chong Wang, Yuanhong Chen, Yuyuan Liu, Yu Tian, Fengbei Liu, Davis J. McCarthy et al.
      Pages 14-24

    4. Deep is a Luxury We Don’t Have

      • Ahmed Taha, Yen Nhi Truong Vu, Brent Mombourquette, Thomas Paul Matthews, Jason Su, Sadanand Singh
      Pages 25-35

    5. PD-DWI: Predicting Response to Neoadjuvant Chemotherapy in Invasive Breast Cancer with Physiologically-Decomposed Diffusion-Weighted MRI Machine-Learning Model

      • Maya Gilad, Moti Freiman
      Pages 36-45

    6. Transformer Based Multi-view Network for Mammographic Image Classification

      • Zizhao Sun, Huiqin Jiang, Ling Ma, Zhan Yu, Hongwei Xu
      Pages 46-54

    7. Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer in Mammography

      • Kihyun You, Suho Lee, Kyuhee Jo, Eunkyung Park, Thijs Kooi, Hyeonseob Nam
      Pages 55-64

  3. Colonoscopy

    1. Front Matter

      Pages 65-65
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    2. BoxPolyp: Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations

      • Jun Wei, Yiwen Hu, Guanbin Li, Shuguang Cui, S. Kevin Zhou, Zhen Li
      Pages 67-77

    3. FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

      • Kai-Ni Wang, Yuting He, Shuaishuai Zhuang, Juzheng Miao, Xiaopu He, Ping Zhou et al.
      Pages 78-87

    4. Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

      • Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen et al.
      Pages 88-98

    5. Lesion-Aware Dynamic Kernel for Polyp Segmentation

      • Ruifei Zhang, Peiwen Lai, Xiang Wan, De-Jun Fan, Feng Gao, Xiao-Jian Wu et al.
      Pages 99-109

    6. Stepwise Feature Fusion: Local Guides Global

      • Jinfeng Wang, Qiming Huang, Feilong Tang, Jia Meng, Jionglong Su, Sifan Song
      Pages 110-120

    7. Stay Focused - Enhancing Model Interpretability Through Guided Feature Training

      • Alexander C. Jenke, Sebastian Bodenstedt, Martin Wagner, Johanna M. Brandenburg, Antonia Stern, Lars Mündermann et al.
      Pages 121-129

    8. On the Uncertain Single-View Depths in Colonoscopies

      • Javier Rodriguez-Puigvert, David Recasens, Javier Civera, Ruben Martinez-Cantin
      Pages 130-140

    9. Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-Modal Representation Consistency

      • Weijie Ma, Ye Zhu, Ruimao Zhang, Jie Yang, Yiwen Hu, Zhen Li et al.
      Pages 141-150

    10. TGANet: Text-Guided Attention for Improved Polyp Segmentation

      • Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, Sharib Ali
      Pages 151-160

  4. Computer Aided Diagnosis

    1. Front Matter

      Pages 161-161
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    2. SATr: Slice Attention with Transformer for Universal Lesion Detection

      • Han Li, Long Chen, Hu Han, S. Kevin Zhou
      Pages 163-174
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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

Keywords

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.

 




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

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