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This work presents a novel label-efficient selfsupervised representationlearning-based approach for classifying diabetic retinopathy (DR) images incross-domain settings. Most of the existing DR image classification methods arebased on supervised learning which requires a lot of time-consuming andexpensive medical domain experts-annotated data for training. The proposedapproach uses the prior learning from the source DR image dataset to classifyimages drawn from the target datasets. The image representations learned fromthe unlabeled source domain dataset through contrastive learning are used toclassify DR images from the target domain dataset. Moreover, the proposedapproach requires a few labeled images to perform successfully on DR imageclassification tasks in cross-domain settings. The proposed work experimentswith four publicly available datasets: EyePACS, APTOS 2019, MESSIDOR-I, andFundus Images for self-supervised representation learning-based DR imageclassification in cross-domain settings. The proposed method achievesstate-of-the-art results on binary and multiclassification of DR images, evenin cross-domain settings. The proposed method outperforms the existing DR imagebinary and multi-class classification methods proposed in the literature. Theproposed method is also validated qualitatively using class activation maps,revealing that the method can learn explainable image representations. Thesource code and trained models are published on GitHub.

Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images