  Noisy data and the similarity in the ocular appearances caused by different
ophthalmic pathologies pose significant challenges for an automated expert
system to accurately detect retinal diseases. In addition, the lack of
knowledge transferability and the need for unreasonably large datasets limit
clinical application of current machine learning systems. To increase
robustness, a better understanding of how the retinal subspace deformations
lead to various levels of disease severity needs to be utilized for
prioritizing disease-specific model details. In this paper we propose the use
of disease-specific feature representation as a novel architecture comprised of
two joint networks -- one for supervised encoding of disease model and the
other for producing attention maps in an unsupervised manner to retain disease
specific spatial information. Our experimental results on publicly available
datasets show the proposed joint-network significantly improves the accuracy
and robustness of state-of-the-art retinal disease classification networks on
unseen datasets.
