RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images

This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs. Unlike traditional approaches that rely on single-view imaging data and face challenges in generalizing across diverse c...

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Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: Chen, Ze, Zhang, Gongyu, Huo, Jiayu, Joan Nunez do Rio, Komninos, Charalampos, Liu, Yang, Sparks, Rachel, Ourselin, Sebastien, Bergeles, Christos, Jackson, Timothy
Format: Article
Language:English
Subjects:
Adaptation
Image enhancement
Machine learning
Medical imaging
Model accuracy
Optimization
Testing time
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Summary:This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs. Unlike traditional approaches that rely on single-view imaging data and face challenges in generalizing across diverse clinical settings, our method leverages the rich information in the unlabelled multi-view imaging data to improve model robustness and accuracy. By incorporating a class balancing method, a test-time adaptation technique and a multi-view optimization strategy, we address the critical issue of domain shift that often hampers the performance of machine learning models in real-world applications. Experiments comparing various state-of-the-art domain generalization and test-time optimization methodologies show that our approach consistently outperforms when combined with existing baseline and state-of-the-art methods. We also show our online method improves all existing techniques. Our framework demonstrates improvements in domain generalization capabilities and offers a practical solution for real-world deployment by facilitating online adaptation to new, unseen datasets. Our code is available at https://github.com/zgy600/RetiGen .
ISSN:2331-8422