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Mitosis Domain Generalization and Diabetic Retinopathy Analysis

MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Bin Sheng

    1. Shanghai Jiao Tong University, Shanghai, China

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  2. Marc Aubreville

    1. Technische Hochschule Ingolstadt, Ingolstadt, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13597)

Included in the following conference series:

Conference proceedings info: DRAC 2022, MIDOG 2022.

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Table of contents (25 papers)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. DRAC

    1. Front Matter

      Pages 1-4
      Download chapter PDF

    2. nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy Analysis

      • Felix Krause, Dominik Heindl, Hana Jebril, Markus Karner, Markus Unterdechler
      Pages 5-15

    3. Automated Analysis of Diabetic Retinopathy Using Vessel Segmentation Maps as Inductive Bias

      • Linus Kreitner, Ivan Ezhov, Daniel Rueckert, Johannes C. Paetzold, Martin J. Menten
      Pages 16-25

    4. Bag of Tricks for Diabetic Retinopathy Grading of Ultra-Wide Optical Coherence Tomography Angiography Images

      • Renyu Li, Yunchao Gu, Xinliang Wang, Sixu Lu
      Pages 26-30

    5. Deep Convolutional Neural Network for Image Quality Assessment and Diabetic Retinopathy Grading

      • Zhenyu Chen, Liqin Huang
      Pages 31-37

    6. Diabetic Retinal Overlap Lesion Segmentation Network

      • Zhiqiang Gao, Jinquan Guo
      Pages 38-45

    7. An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images

      • Yuhan Zheng, Fuping Wu, Bartłomiej W. Papież
      Pages 46-58

    8. Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity

      • Gitaek Kwon, Eunjin Kim, Sunho Kim, Seongwon Bak, Minsung Kim, Jaeyoung Kim
      Pages 59-73

    9. Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images

      • Junlin Hou, Fan Xiao, Jilan Xu, Yuejie Zhang, Haidong Zou, Rui Feng
      Pages 74-87

    10. Deep Learning-Based Multi-tasking System for Diabetic Retinopathy in UW-OCTA Images

      • Jungrae Cho, Byungeun Shon, Sungmoon Jeong
      Pages 88-96

    11. Semi-supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment

      • Zhuoyi Tan, Hizmawati Madzin, Zeyu Ding
      Pages 97-117

    12. Image Quality Assessment Based on Multi-model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA Images

      • Zhuoyi Tan, Hizmawati Madzin, Zeyu Ding
      Pages 118-126

    13. An Improved U-Net for Diabetic Retinopathy Segmentation

      • Xin Chen, Yanbin Chen, Chaonan Lin, Lin Pan
      Pages 127-134

    14. A Vision Transformer Based Deep Learning Architecture for Automatic Diagnosis of Diabetic Retinopathy in Optical Coherence Tomography Angiography

      • Sungjin Choi, Bosoung Jeoun, Jaeyoung Anh, Jaehyup Jeong, Yongjin Choi, Dowan Kwon et al.
      Pages 135-145

    15. Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy

      • Yihao Li, Rachid Zeghlache, Ikram Brahim, Hui Xu, Yubo Tan, Pierre-Henri Conze et al.
      Pages 146-160

    16. Data Augmentation by Fourier Transformation for Class-Imbalance: Application to Medical Image Quality Assessment

      • Zhicheng Wu, Yanbin Chen, Xuru Zhang, Liqin Huang
      Pages 161-169

    17. Automatic Image Quality Assessment and DR Grading Method Based on Convolutional Neural Network

      • Wen Zhang, Hao Chen, Daisong Li, Shaohua Zheng
      Pages 170-177

    18. A Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy Grading

      • Xiaochao Yan, Zhaopei Li, Jianhui Wen, Lin Pan
      Pages 178-185

    19. Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images Using Transfer Learning

      • Farhana Sultana, Abu Sufian, Paramartha Dutta
      Pages 186-194
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  1. Mitosis Domain Generalization and Diabetic Retinopathy Analysis

Keywords

About this book

This book constitutes two challenges that were held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which took place in Singapore during September 18-22, 2022. 

The peer-reviewed 20 long and 5 short papers included in this volume stem from the following three biomedical image analysis challenges:

  •          Mitosis Domain Generalization Challenge (MIDOG 2022),
  •          Diabetic Retinopathy Analysis Challenge (CRAC 2022)

The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.


Editors and Affiliations

  • Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  • Technische Hochschule Ingolstadt, Ingolstadt, Germany

    Marc Aubreville

Bibliographic Information

  • Book Title: Mitosis Domain Generalization and Diabetic Retinopathy Analysis

  • Book Subtitle: MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings

  • Editors: Bin Sheng, Marc Aubreville

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-33658-4

  • 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 2023

  • Softcover ISBN: 978-3-031-33657-7Published: 30 May 2023

  • eBook ISBN: 978-3-031-33658-4Published: 29 May 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: IX, 242

  • Number of Illustrations: 25 b/w illustrations, 60 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Computing Milieux, Computer Applications, Machine Learning

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