Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributional Shifts in Automated Diabetic Retinopathy Screening   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.

26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
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The categories of this paper are: eess
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol