Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.

Objective: Diseases such as age-related macular degeneration (AMD) are classified based on human rubrics that are prone to bias. Supervised neural networks trained using human-generated labels require labor-intensive annotations and are restricted to specific trained tasks. Here, we trained a self-supervised deep learning network using unlabeled fundus images, enabling data-driven feature classification of AMD severity and discovery of ocular phenotypes.

The natural statistics of blur.

Blur from defocus can be both useful and detrimental for visual perception: It can be useful as a source of depth information and detrimental because it degrades image quality. We examined these aspects of blur by measuring the natural statistics of defocus blur across the visual field. Participants wore an eye-and-scene tracker that measured gaze direction, pupil diameter, and scene distances as they performed everyday tasks.