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Image-Based Prediction of Retinal Disease Progression

MICCAI Challenges, DIAMOND 2024 and MARIO 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

  • Conference proceedings
  • © 2025

Overview

Editors:
  1. Gwenolé Quellec

    1. Inserm, Brest, France

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  2. Mostafa El Habib Daho

    1. Université de Bretagne Occidentale, Brest, France

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  3. Rachid Zeghlache

    1. Université de Bretagne Occidentale, Brest, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15503)

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About this book

This book constitutes the proceedings from the MICCAI Challenges, Device-Independent Diabetic Macular Edema Onset Prediction, DIAMOND 2024, and Monitoring Age-Related macular degeneration progression in Optical coherence tomography, MARIO 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024.

The 15 papers included in this book from MARIO 2024 were carefully reviewed and selected from 17 submissions, whereas the 6 papers included here from DIAMOND 2024 were carefully reviewed and selected from 8 submissions. These papers focus on a wide range of state-of-the-art deep learning approaches to derive patient specific rules for Diabetic retinopathy (DR) and age-related macular degeneration (AMD) progression prediction from retinal images.

Keywords

Table of contents (21 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. DIAMOND Challenge

    1. Front Matter

      Pages 1-1
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    2. AI-Based Diagnostic Model for Predicting ci-DME Development

      • Bo Yang, Yangyang Yan, Wencheng Miao
      Pages 3-19

    3. Automatic Prediction of Center-Involved Diabetic Macular Edema Using Ultra-Widefield Color Fundus Photography

      • Philippe Zhang, Yihao Li, Weili Jiang, Jing Zhang, Sarah Matta, Yubo Tan et al.
      Pages 20-29

    4. A Device-Agnostic Deep Learning Approach for Predicting Ci-DME Onset Using UWF-CFP Images

      • Amin Khouani, Ihsane Mekki
      Pages 30-40

    5. Onset Prediction of Center-Involved Diabetic Macular Edema on Ultra-Widefield Fundus Images Using Finetuned ResNet with Class Balanced Focal Loss

      • Yasine Lehmiani, Ayoub Elkhouzari, Abdelhak Mahmoudi
      Pages 41-53

    6. Calibrated Models for DME Progression Prediction from Ultra-Wide Field Retinal Images

      • Adrian Galdran
      Pages 54-63

    7. Deep Learning Ensemble for Predicting Diabetic Macular Edema Onset Using Ultra-Wide Field Color Fundus Image

      • Pengyao Qin, Arun Thirunavukarasu, Theodoros Arvanitis, Le Zhang
      Pages 64-73

  3. MARIO Challenge

    1. Front Matter

      Pages 75-75
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    2. Deep Learning Approaches for Monitoring Age-Related Macular Degeneration Progression in Optical Coherence Tomography

      • Yiding Hao
      Pages 77-86

    3. Leveraging MaxVit on Fused OCT Scan Pairs for Age-Related Macular Degeneration Evolution Assessment

      • Yosuke Yamagishi
      Pages 87-96

    4. Patch Progression Masked Autoencoder with Fusion CNN Network for Classifying Evolution Between Two Pairs of 2D OCT Slices

      • Philippe Zhang, Weili Jiang, Yihao Li, Jing Zhang, Sarah Matta, Yubo Tan et al.
      Pages 97-105

    5. Automatic Detection and Prediction of nAMD Activity Change in Retinal OCT Using Siamese Networks and Wasserstein Distance for Ordinality

      • Taha Emre, Teresa Araújo, Marzieh Oghbaie, Dmitrii Lachinov, Guilherme Aresta, Hrvoje Bogunović
      Pages 106-117

    6. Exploring the Use of Off-the-Shelf AI Models for Complex Medical Tasks: ResNet18 for Predicting Age-Related Macular Degeneration

      • Amerens A. Bekkers, Nina M. van Liebergen, Hugo J. Kuijf
      Pages 118-125

    7. AI-Driven Analysis of Sequential OCT Images for Detecting Neovascular Activity in Age-Related Macular Degeneration to Optimize Anti-VEGF Therapy

      • Yi Ding
      Pages 126-134

    8. Efficient Deep Learning Models for Evaluating the Progression of Age-Related Macular Degeneration Through Optical Coherence Tomography

      • Abdul Qayyum, Moona Mazher, Imran Razzak, Steven A. Niederer
      Pages 135-144

    9. Optimizing Anti-VEGF Treatment Strategies with AI-Based Neovascular AMD Detection

      • Yi Ding
      Pages 145-153

    10. Monitoring Age-Related Macular Degeneration Progression in Optical Coherence Tomography (MARIO), Task 1 - MICCAI Challenge 2024, jkulinzstudents Submission

      • Marcel Huber, Patrick Binder, Markus Frohmann
      Pages 154-162

    11. Monitoring Age-Related Macular Degeneration Progression in Optical Coherence Tomography (MARIO), Task 2 - MICCAI Challenge 2024, jkulinzstudents Submission

      • Patrick Binder, Marcel Huber, Markus Frohmann
      Pages 163-171

    12. Modular Transformer-Based Monitoring and Prediction of Wet AMD Progression Using OCT Imaging

      • Lovre Antonio Budimir, Ivana Matovinović, Donik Vršnak, Sven Lončarić
      Pages 172-181
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Other volumes

  1. Image-Based Prediction of Retinal Disease Progression

Editors and Affiliations

  • Inserm, Brest, France

    Gwenolé Quellec

  • Université de Bretagne Occidentale, Brest, France

    Mostafa El Habib Daho, Rachid Zeghlache

Bibliographic Information

  • Book Title: Image-Based Prediction of Retinal Disease Progression

  • Book Subtitle: MICCAI Challenges, DIAMOND 2024 and MARIO 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

  • Editors: Gwenolé Quellec, Mostafa El Habib Daho, Rachid Zeghlache

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-86651-7

  • 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 2025

  • Softcover ISBN: 978-3-031-86650-0Published: 27 April 2025

  • eBook ISBN: 978-3-031-86651-7Published: 26 April 2025

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 224

  • Number of Illustrations: 16 b/w illustrations, 56 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Biomedical Engineering and Bioengineering, Machine Learning

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