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.
@article{arxiv.2107.11822,
title = {Distributional Shifts in Automated Diabetic Retinopathy Screening},
author = {Jay Nandy and Wynne Hsu and Mong Li Lee},
journal= {arXiv preprint arXiv:2107.11822},
year = {2021}
}