Distributional Shifts in Automated Diabetic Retinopathy Screening

@article{Nandy2021DistributionalSI,
  title={Distributional Shifts in Automated Diabetic Retinopathy Screening},
  author={Jay Nandy and Wynne Hsu and Mong Li Lee},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11822}
}
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… Expand

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