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Despite the revolutionary impact of AI and the development of locally trainedalgorithms, achieving widespread generalized learning from multi-modal data inmedical AI remains a significant challenge. This gap hinders the practicaldeployment of scalable medical AI solutions. Addressing this challenge, ourresearch 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 arecombined enabling a more comprehensive range of representation. Our methodaddresses the issue using a two-phase training approach that combinesself-supervised pretraining and supervised fine-tuning with a mask autoencoderbased on the SwinV2 backbone by providing a solution for real-world clinicaldeployment. Extensive experiments on three datasets with different encoderbackbones, low data settings, unseen data settings, and the effect ofaugmentation show that our method outperforms the baseline model, Resnet-50 byconsistently attaining AUC-ROC performance surpassing 77% across all tests,whereas the baseline model exceeds 54%. Moreover, in terms of the AUC-PRmetric, our proposed method exceeded 42%, showcasing a substantial increase ofat least 10% in performance compared to the baseline, which exceeded only 33%.This contributes to our understanding of our approach's potential andemphasizes its usefulness in clinical settings.

OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection