  Many diseases are classified based on human-defined rubrics that are prone to
bias. Supervised neural networks can automate the grading of retinal fundus
images, but require labor-intensive annotations and are restricted to the
specific trained task. Here, we employed an unsupervised network with
Non-Parametric Instance Discrimination (NPID) to grade age-related macular
degeneration (AMD) severity using fundus photographs from the Age-Related Eye
Disease Study (AREDS). Our unsupervised algorithm demonstrated versatility
across different AMD classification schemes without retraining, and achieved
unbalanced accuracies comparable to supervised networks and human
ophthalmologists in classifying advanced or referable AMD, or on the 4-step AMD
severity scale. Exploring the networks behavior revealed disease-related fundus
features that drove predictions and unveiled the susceptibility of more
granular human-defined AMD severity schemes to misclassification by both
ophthalmologists and neural networks. Importantly, unsupervised learning
enabled unbiased, data-driven discovery of AMD features such as geographic
atrophy, as well as other ocular phenotypes of the choroid, vitreous, and lens,
such as visually-impairing cataracts, that were not pre-defined by human
labels.
