June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Unsupervised deep learning for grading age-related macular degeneration using retinal fundus images
Author Affiliations & Notes
  • Glenn Yiu
    Ophthalmology & Vision Science, University of California Davis, Sacramento, California, United States
  • Baladitya Yellapragada
    Vision Science, University of California Berkeley, Berkeley, California, United States
  • Sascha Hornauer
    Vision Science, University of California Berkeley, Berkeley, California, United States
  • Kiersten Snyder
    Ophthalmology & Vision Science, University of California Davis, Sacramento, California, United States
  • Stella Yu
    Vision Science, University of California Berkeley, Berkeley, California, United States
  • Footnotes
    Commercial Relationships   Glenn Yiu, Allergan (C), Carl Zeiss Meditec (C), Clearside Biomedical (C), Genentech (C), Intergalactic Therapeutics (C), Iridex (C), Regeneron (C), Topcon (C), Verily (C); Baladitya Yellapragada, None; Sascha Hornauer, None; Kiersten Snyder, None; Stella Yu, Amazon Research (F), Cerebral Therapeutics (C), Etegent Technologies (F), Facebook AI Research (F), Glidewell Laboratories (F), Google Research (F), Gosch Research (F), iCueMotion (C), Intel Research (C), KLA Tencor (C), Leidos (F), Samsung (F)
  • Footnotes
    Support  CITRIS/Banatao Institute
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 119. doi:
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    • Get Citation

      Glenn Yiu, Baladitya Yellapragada, Sascha Hornauer, Kiersten Snyder, Stella Yu; Unsupervised deep learning for grading age-related macular degeneration using retinal fundus images. Invest. Ophthalmol. Vis. Sci. 2021;62(8):119.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Many diseases such as age-related macular degeneration (AMD) are classified based on human-defined rubrics that are prone to bias. Supervised neural networks are trained using human-generated labels that require labor-intensive annotations and are restricted to the specific trained tasks. Here, we employ unsupervised learning which organizes fundus images based only on visual similarity to determine AMD severity and identify ocular features without the confines of human definitions or labels.

Methods : We trained an unsupervised deep neural network with Non-Parametric Instance Discrimination (NPID) using 100,848 human-graded fundus images from 4757 participants from the Age-Related Eye Disease Study (AREDS) to grade AMD severity using 2-step, 4-step, and 9-step classification schemes. We compared balanced and unbalanced accuracies of NPID against published supervised networks and ophthalmologists, explored network behavior using hierarchical learning of image subsets and spherical k-means clustering of feature vectors, then searched for ocular features that can be identified without labels.

Results : Unsupervised NPID demonstrated versatility across different AMD classification schemes without re-training, and achieved balanced accuracies comparable to supervised networks or human ophthalmologists in classifying advanced AMD (82% vs. 81% or 89%), referable AMD (87% vs. 92% or 96%), or on the 4-step AMD severity scale (65% vs. 63% or 67%), despite never directly learning these labels. Drusen area drove network predictions on the 4-step scale, while depigmentation and geographic atrophy (GA) areas correlated with advanced AMD classes. Unsupervised learning identified grader-mislabeled images and revealed susceptibility of some classes within the more granular 9-step AMD scale to misclassification by both ophthalmologists and neural networks. Importantly, unsupervised learning enabled data-driven discovery of AMD features such as GA and other ocular phenotypes of the choroid (e.g. tessellated or blonde fundi), vitreous (e.g. asteroid hyalosis), and lens (e.g. nuclear cataracts), that were not pre-defined by human labels.

Conclusions : Unsupervised learning enables automated AMD severity grading comparable to ophthalmologists and supervised networks, reveals biases of human-defined AMD classification systems, and allows unbiased, data-driven discovery of AMD and non-AMD ocular phenotypes.

This is a 2021 ARVO Annual Meeting abstract.

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