  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.
