Despite the revolutionary impact of AI and the development of locally trained algorithms, achieving widespread generalized learning from multi-modal data in medical AI remains a significant challenge. This gap hinders the practical deployment of scalable medical AI solutions. Addressing this challenge, our research contributes a self-supervised robust machine learning framework, OCT-SelfNet, for detecting eye diseases using optical coherence tomography (OCT) images. In this work, various data sets from various institutions are combined enabling a more comprehensive range of representation. Our method addresses the issue using a two-phase training approach that combines self-supervised pretraining and supervised fine-tuning with a mask autoencoder based on the SwinV2 backbone by providing a solution for real-world clinical deployment. Extensive experiments on three datasets with different encoder backbones, low data settings, unseen data settings, and the effect of augmentation show that our method outperforms the baseline model, Resnet-50 by consistently attaining AUC-ROC performance surpassing 77% across all tests, whereas the baseline model exceeds 54%. Moreover, in terms of the AUC-PR metric, our proposed method exceeded 42%, showcasing a substantial increase of at least 10% in performance compared to the baseline, which exceeded only 33%. This contributes to our understanding of our approach's potential and emphasizes its usefulness in clinical settings.

我们的研究提出了一种自我监督的强大机器学习框架OCT-SelfNet，用于使用光学相干断层扫描(OCT)图像检测眼部疾病，通过结合多个机构的数据集，采用两阶段训练方法（自我监督预训练和有监督微调），并基于SwinV2主干结构的遮罩自编码器解决了实际临床应用部署的问题。广泛的实验表明，我们的方法在所有测试中均获得超过77%的AUC-ROC性能，而基线模型Resnet-50的性能不到54%。此外，根据AUC-PR指标，我们的方法达到了42%以上的性能，相比基线模型的33%，性能提升了至少10%，这显示了我们方法的潜力和在临床设置中的实用性。

OCT-SelfNet: 用于广义和稳健视网膜疾病检测的自监督多模态数据框架