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arXiv · 2107.11822

Distributional Shifts in Automated Diabetic Retinopathy Screening

Jay Nandy·Wynne Hsu·Mong Li Lee
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Abstract

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.

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BibTeXRIS

Jay Nandy, Wynne Hsu, Mong Li Lee. 2021-07-25. Distributional Shifts in Automated Diabetic Retinopathy Screening. https://arxiv.org/abs/2107.11822

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