Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

@article{Abrmoff2016ImprovedAD,
  title={Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.},
  author={Michael D. Abr{\`a}moff and Yiyue Lou and Ali Erginay and Warren Clarida and Ryan E. Amelon and James C. Folk and Meindert Niemeijer},
  journal={Investigative ophthalmology & visual science},
  year={2016},
  volume={57 13},
  pages={5200-5206}
}
Purpose To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. Methods We used the previously reported consensus reference standard of referable DR (rDR… CONTINUE READING
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