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Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

Worrall, DE; Wilson, C; Brostow, GJ; (2016) Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks. In: Carneiro, G, (ed.) Deep Learning and Data Labeling for Medical Applications. LABELS 2016, DLMIA 2016. (pp. pp. 68-76). Springer, Cham Green open access

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Abstract

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.

Type: Proceedings paper
Title: Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks
Event: Workshop on Deep Learning in Medical Image Analysis
Location: Athens, Greece
Dates: 17 October 2016 - 21 October 2016
ISBN-13: 978-3-319-46975-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-46976-8_8
Publisher version: http://dx.doi.org/10.1007/978-3-319-46976-8_8
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10039221
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