  Ophthalmic images may contain identical-looking pathologies that can cause
failure in automated techniques to distinguish different retinal degenerative
diseases. Additionally, reliance on large annotated datasets and lack of
knowledge distillation can restrict ML-based clinical support systems'
deployment in real-world environments. To improve the robustness and
transferability of knowledge, an enhanced feature-learning module is required
to extract meaningful spatial representations from the retinal subspace. Such a
module, if used effectively, can detect unique disease traits and differentiate
the severity of such retinal degenerative pathologies. In this work, we propose
a robust disease detection architecture with three learning heads, i) A
supervised encoder for retinal disease classification, ii) An unsupervised
decoder for the reconstruction of disease-specific spatial information, and
iii) A novel representation learning module for learning the similarity between
encoder-decoder feature and enhancing the accuracy of the model. Our
experimental results on two publicly available OCT datasets illustrate that the
proposed model outperforms existing state-of-the-art models in terms of
accuracy, interpretability, and robustness for out-of-distribution retinal
disease detection.
