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.
The original version of this chapter was revised: Acknowledgement section has been updated. The erratum to this chapter is available at DOI: 10.1007/978-3-319-46976-8_29
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-46976-8_29
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Notes
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Neonatal fundus imaging quality has not improved since, only the labels are different.
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Acknowledgement
We would like to thank Dr Anna Ells of Alberta Childrens Hospital, Calgary, Canada for the ‘Canada dataset’, and Alistair Fielder and Philip ‘Eddie’ Edwards for insightful conversations. Daniel Worrall is supported by Fight for Sight, registered charity number 1111438.
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Worrall, D.E., Wilson, C.M., Brostow, G.J. (2016). Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_8
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