RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
<|reference_start|>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 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 .<|reference_end|>
@article{chen2024retigen:,
    title={RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View
  Fundus Images},
    author={Ze Chen, Gongyu Zhang, Jiayu Huo, Joan Nunez do Rio, Charalampos
  Komninos, Yang Liu, Rachel Sparks, Sebastien Ourselin, Christos Bergeles,
  Timothy Jackson},
    journal={arXiv preprint arXiv:2403.15647},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15647},
    primaryClass={cs.CV}
}