Vasha DuTell and Baladitya Yellapragada

Speakers

Vasha DuTell
VS Graduate Student, Olshausen Lab

Baladitya Yellapragada
VS Graduate Student, Yu Lab

Date and Time

Monday, November 30, 2020
11:10 am - 12:30 pm

Location

489 Minor Hall
Berkeley, CA

Vasha DuTell's Abstract

The Spatiotemporal Power Spectrum of Natural Human Vision

When engaging in natural tasks, the human visual system processes a highly dynamic visual data stream. The retina, performing the very first steps in this processing, is thought to be adapted to take advantage of low-level signal regularities, such as the autocorrelation function or power spectrum, to produce a more efficient encoding of the data (Atick & Redlich, 1992). Previous work examined the joint spatio-temporal power spectrum of handheld camera videos and Hollywood movies, showing that power falls as an inverse power-law function of spatial and temporal frequency, with an inseparable relationship (Dong & Attick, 1995). However, these data are far from a true characterization of a day in the life of the retina due to body, head and eye motion. In addition, the distribution of natural tasks will influence the statistics of this signal. Here, we aim to characterize these statistics of natural vision using a custom device that consists of a head-mounted eye tracker coupled with high frame-rate world cameras and orientation sensors. Using video data captured from this setup, we analyze the joint spatiotemporal power spectrum for three conditions: 1) a static camera viewing a natural task being performed, 2) a head mounted camera worn by a subject engaged in a natural task, and 3) videos simulating the dynamic retinal image, created by overlaying the subject's eye motions on the head-mounted camera video stream. Results suggest that compared to a static camera, body and head motion have the effect of boosting high temporal frequencies. Eye motion enhances this effect, particularly for mid to high spatial frequencies, causing this portion to deviate from the power law and become nearly flat. These data will be important for developing efficient coding models relevant to natural vision.

Baladitya Yellapragada's Abstract

Unsupervised deep learning for grading age-related macular degeneration using retinal fundus images

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 network’s 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.