  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 .
