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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Anne L. Martel

    1. University of Toronto, Toronto, Canada

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  2. Purang Abolmaesumi

    1. The University of British Columbia, Vancouver, Canada

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  3. Danail Stoyanov

    1. University College London, London, UK

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  4. Diana Mateus

    1. École Centrale de Nantes, Nantes, France

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  5. Maria A. Zuluaga

    1. EURECOM, Biot, France

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  6. S. Kevin Zhou

    1. Chinese Academy of Sciences, Beijing, China

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  7. Daniel Racoceanu

    1. Sorbonne University, Paris, France

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  8. Leo Joskowicz

    1. The Hebrew University of Jerusalem, Jerusalem, Israel

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12261)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: MICCAI 2020.

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Table of contents (81 papers)

  1. Front Matter

    Pages i-xxxvii
    Download chapter PDF

  2. Machine Learning Methodologies

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

      • Haohan Li, Zhaozheng Yin
      Pages 3-13

    3. Scribble2Label: Scribble-Supervised Cell Segmentation via Self-generating Pseudo-Labels with Consistency

      • Hyeonsoo Lee, Won-Ki Jeong
      Pages 14-23

    4. Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides

      • Christian Marzahl, Christof A. Bertram, Marc Aubreville, Anne Petrick, Kristina Weiler, Agnes C. Gläsel et al.
      Pages 24-32

    5. Deep Reinforcement Active Learning for Medical Image Classification

      • Jingwen Wang, Yuguang Yan, Yubing Zhang, Guiping Cao, Ming Yang, Michael K. Ng
      Pages 33-42

    6. An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition

      • Li Quan, Yan Li, Xiaoyi Chen, Ni Zhang
      Pages 43-52

    7. Synthetic Sample Selection via Reinforcement Learning

      • Jiarong Ye, Yuan Xue, L. Rodney Long, Sameer Antani, Zhiyun Xue, Keith C. Cheng et al.
      Pages 53-63

    8. Dual-Level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT

      • Wenzhe Wang, Qingyu Song, Jiarong Zhou, Ruiwei Feng, Tingting Chen, Wenhao Ge et al.
      Pages 64-73

    9. BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture

      • Tiange Xiang, Chaoyi Zhang, Dongnan Liu, Yang Song, Heng Huang, Weidong Cai
      Pages 74-84

    10. Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net

      • Weiyang Shi, Kaibin Xu, Ming Song, Lingzhong Fan, Tianzi Jiang
      Pages 85-94

    11. Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data

      • Xiao Liu, Sotirios A. Tsaftaris
      Pages 95-105

    12. Learning and Exploiting Interclass Visual Correlations for Medical Image Classification

      • Dong Wei, Shilei Cao, Kai Ma, Yefeng Zheng
      Pages 106-115

    13. Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation

      • Rasha Sheikh, Thomas Schultz
      Pages 116-126

    14. Deep kNN for Medical Image Classification

      • Jiaxin Zhuang, Jiabin Cai, Ruixuan Wang, Jianguo Zhang, Wei-Shi Zheng
      Pages 127-136

    15. Learning Semantics-Enriched Representation via Self-discovery, Self-classification, and Self-restoration

      • Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael B. Gotway, Jianming Liang
      Pages 137-147

    16. DECAPS: Detail-Oriented Capsule Networks

      • Aryan Mobiny, Pengyu Yuan, Pietro Antonio Cicalese, Hien Van Nguyen
      Pages 148-158

    17. Federated Simulation for Medical Imaging

      • Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F. Frangi, Sanja Fidler
      Pages 159-168

    18. Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy

      • Zhuoyun Li, Changhong Zhong, Ruixuan Wang, Wei-Shi Zheng
      Pages 169-178

    19. Learning to Segment When Experts Disagree

      • Le Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof et al.
      Pages 179-190
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  8. Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data

Keywords

About this book

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.

The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:

Part I: machine learning methodologies

Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks

Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis

Part IV: segmentation; shape models and landmark detection

Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology

Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging

Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Editors and Affiliations

  • University of Toronto, Toronto, Canada

    Anne L. Martel

  • The University of British Columbia, Vancouver, Canada

    Purang Abolmaesumi

  • University College London, London, UK

    Danail Stoyanov

  • École Centrale de Nantes, Nantes, France

    Diana Mateus

  • EURECOM, Biot, France

    Maria A. Zuluaga

  • Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  • Sorbonne University, Paris, France

    Daniel Racoceanu

  • The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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