Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

Published in Deep Learning and Data Labeling for Medical Applications, Springer, 2016

Retinopathy of Prematurity (ROP) is an ocular disease observed in premature babies, considered one of the largest preventable causes of childhood blindness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP detector and with small modifications also return an approximate Bayesian posterior over disease presence. To the best of our knowledge, this is the first completely automated ROP detection system. (2) To further aid grading, we train a second CNN to return novel feature map visualizations of pathologies, learned directly from the data. These feature maps highlight discriminative information, which we believe may be used by clinicians with our classifier to aid in screening.

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  1. @inproceedings{WorrallWB2016,
      title = {Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks},
      author = {Worrall, Daniel E and Wilson, Clare M and Brostow, Gabriel J},
      booktitle = {International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis},
      pages = {68--76},
      year = {2016},
      organization = {Springer}