  Deep learning-based models are developed to automatically detect if a retina
image is `referable' in diabetic retinopathy (DR) screening. However, their
classification accuracy degrades as the input images distributionally shift
from their training distribution. Further, even if the input is not a retina
image, a standard DR classifier produces a high confident prediction that the
image is `referable'. Our paper presents a Dirichlet Prior Network-based
framework to address this issue. It utilizes an out-of-distribution (OOD)
detector model and a DR classification model to improve generalizability by
identifying OOD images. Experiments on real-world datasets indicate that the
proposed framework can eliminate the unknown non-retina images and identify the
distributionally shifted retina images for human intervention.
