Proceedings

Multimodal Retina Image Analysis Survey: Datasets, Tasks and Methods

Hongwei Sheng, Heming Du, Xin Shen, Sen Wang, Xin Yu

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Survey Track. Pages 10650-10659.

https://doi.org/10.24963/ijcai.2025/1182

Keywords

  • Computer Vision: CV: Multimodal learning
  • Multidisciplinary Topics and Applications: MTA: Health and medicine

Abstract

Retina images provide a noninvasive view of the central nervous system and microvasculature, making it essential for clinical applications.

Changes in the retina often indicate both ophthalmic and systemic diseases, aiding in diagnosis and early intervention.

While deep learning algorithms have advanced retina image analysis, a comprehensive review of related datasets, tasks, and benchmarking is still lacking.

In this survey, we systematically categorize existing retina image datasets based on their available data modalities, and review the tasks these datasets support in multimodal retina image analysis.

We also explain key evaluation metrics used in various retina image analysis benchmarks.

By thoroughly examining current datasets and methods, we highlight the challenges and limitations in existing benchmarks and discuss potential research topics in the field.

We hope this work will guide future retina analysis methods and promote the shared use of existing data across different tasks.

Hongwei Sheng, Heming Du, Xin Shen, Sen Wang, and Xin Yu. 2025. Multimodal Retina Image Analysis Survey: Datasets, Tasks and Methods. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10650–10659. https://doi.org/10.24963/ijcai.2025/1182
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