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