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Ultra-Widefield Fundus Imaging for Diabetic Retinopathy

First MICCAI Challenge, UWF4DR 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

  • Conference proceedings
  • © 2025

Overview

Editors:
  1. Bin Sheng

    1. Shanghai Jiao Tong University, Shanghai, China

  2. Hao Chen

    1. Hong Kong University of Science and Technology, Hong Kong, China

  3. Tien Yin Wong

    1. Tsinghua University, Beijing, China

  4. Carol Y. Cheung

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

  5. Bo Qian

    1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15597)

Included in the following conference series:

Conference proceedings info: UWF4DR 2024.

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About this book

This book constitutes the proceedings of the First MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, held in Marrakesh, Morocco, on October 10, 2024.

The 17 full papers included in this book were carefully reviewed and selected from 17 submissions. They present methodologies and results of the challenge which consists of three clinically relevant subtasks: image quality assessment for ultra-widefield fundus (Task 1), identification of referable diabetic retinopathy (Task 2), and identification of diabetic macular edema (Task 3).

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Table of contents (17 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Image Quality Assessment with Model Fusion for Ultra-widefield Fundus

    • Bo Yang, Yangyang Yan, Di Liu, Qicheng Li, Yue Zhang
    Pages 1-9

  3. AI Algorithm for Ultra-widefield Fundus Imaging for Diabetic Retinopathy - RDR, DME

    • Bo Yang, Yue Zhang, Di Liu, Qicheng Li, Yangyang Yan
    Pages 10-17

  4. Lightweight and Accurate: ShuffleNet for Diabetic Retinopathy and EfficientNet for Diabetic Macular Edema Diagnosis

    • Berthold Scheuringer, Moritz Haderer, Martin Marinschek, Oleksandra Menzatiuk, Vera Pils
    Pages 18-35

  5. Efficient Deep Learning Models for Ultra-widefield Fundus Imaging for Diabetic Retinopathy

    • Abdul Qayyum, Moona Mazher, Steven A. Niederer
    Pages 36-46

  6. Bag of Tricks for Ultra-widefield Fundus Image Quality Assessment

    • Junfeng Sun, Xinliang Wang, Yunchao Gu
    Pages 47-54

  7. Bag of Tricks for Diabetic Retinopathy and Diabetic Macular Edema Classification in Ultra-widefield Imaging

    • Hyeonmin Kim, Yunnie Cho, Ohhyun Kwon, Dongha Lee
    Pages 55-62

  8. Deep Self-supervised Learning for Ultra-widefield Fundus Image Quality Assessment

    • Ammar M. Okran, Saif Khalid Musluh, Saddam Abdulwahab, Domenec Puig, Hatem A. Rashwan
    Pages 63-74

  9. Reliable DL-Based Referable Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ultra-widefield Fundus Images

    • Saif Khalid Musluh, Ammar M. Okran, Saddam Abdulwahab, Hatem A. Rashwan, Domenec Puig
    Pages 75-87

  10. Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-widefield Fundus Imaging

    • Philippe Zhang, Pierre-Henri Conze, Mathieu Lamard, Béatrice Cochener, Gwenolé Quellec, Mostafa El Habib Daho
    Pages 88-100

  11. A Comprehensive Approach to Diabetic Retinopathy Classification: Combining ResNet34 with Enhanced Preprocessing for Ultra-widefield Fundus Imaging

    • Yeon Su Park, Ji Hye Won
    Pages 101-109

  12. An Ultra-efficient Method for Real-Time Ultra-widefield Fundus Image Quality Assessment

    • Justin Engelmann, Lucas Gago
    Pages 110-117

  13. Ultra-fast Detection of Referable Diabetic Retinopathy and Macular Edema in Ultra-widefield Fundus Imaging Using a Unified Risk Score

    • Justin Engelmann, Lucas Gago
    Pages 118-124

  14. Efficient Deep Learning Approaches for Processing Ultra-widefield Retinal Imaging

    • Siwon Kim, Wooyung Yun, Jeongbin Oh, Soomok Lee
    Pages 125-134

  15. EfficientNet-B1 Based Diabetic Retinopathy Detection from Ultra-widefield Fundus Images

    • Monalisa Bakshi, Chandra Sekhar Seelamantula
    Pages 135-143

  16. Many-MobileNet: Multi-model Augmentation for Robust Retinal Disease Classification

    • Hao Wang, Wenhui Zhu, Xuanzhao Dong, Yanxi Chen, Xin Li, Peijie Qiu et al.
    Pages 144-154

  17. DME-MobileNet: Fine-Tuning nnMobileNet for Diabetic Macular Edema Classification

    • Xuanzhao Dong, Yalin Wang, Yanxi Chen, Xin Li, Hao Wang, Peijie Qiu et al.
    Pages 155-164

  18. Automatic Identification Method for Diabetic Macular Edema in Ultra-widefield Fundus Images

    • Heyou Chang, Zhikang Ge, Jian Zhang, Heng Zhang, Hao Zheng
    Pages 165-173

  19. Back Matter

    Pages 175-176
    Download chapter PDF
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Other volumes

  1. Ultra-Widefield Fundus Imaging for Diabetic Retinopathy

Editors and Affiliations

  • Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  • Hong Kong University of Science and Technology, Hong Kong, China

    Hao Chen

  • Tsinghua University, Beijing, China

    Tien Yin Wong

  • Chinese University of Hong Kong, Hong Kong, China

    Carol Y. Cheung

  • Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bo Qian

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Bibliographic Information

  • Book Title: Ultra-Widefield Fundus Imaging for Diabetic Retinopathy

  • Book Subtitle: First MICCAI Challenge, UWF4DR 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

  • Editors: Bin Sheng, Hao Chen, Tien Yin Wong, Carol Y. Cheung, Bo Qian

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-89388-9

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0), Springer Nature Proceedings Computer Science

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

  • Softcover ISBN: 978-3-031-89387-2Published: 14 May 2025

  • eBook ISBN: 978-3-031-89388-9Published: 13 May 2025

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 176

  • Number of Illustrations: 1 b/w illustrations, 63 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics

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