Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

1. Front Matter

   Pages i-xxxvii

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2. Machine Learning Methodologies

   1. Front Matter

      Pages 1-1

      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

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

• artificial intelligence
• bioinformatics
• computer vision
• deep learning
• image analysis
• image processing
• image reconstruction
• image segmentation
• imaging systems
• learning
• machine learning
• medical images
• neural networks
• pattern recognition
• segmentation methods

• 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

Policies and ethics