251 citations · 846 across the 9 of their papers we have counts for
9 papers
Using generative AI to investigate medical imagery models and datasets
Oran Lang, Doron Yaya-Stupp, Ilana Traynis +13
AI models have shown promise in many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in o…
Discovering novel systemic biomarkers in photos of the external eye
Boris Babenko, Ilana Traynis, Christina Chen +16
External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about…
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
Babak Alipanahi, Farhad Hormozdiari, Babak Behsaz +10
Genome-wide association studies (GWAS) require accurate cohort phenotyping, but expert labeling can be costly, time-intensive, and variable. Here we develop a machine learning (ML)…
Detecting hidden signs of diabetes in external eye photographs
Boris Babenko, Akinori Mitani, Ilana Traynis +10
Diabetes-related retinal conditions can be detected by examining the posterior of the eye. By contrast, examining the anterior of the eye can reveal conditions affecting the front…
Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation
Joy Hsu, Sonia Phene, Akinori Mitani +4
As machine learning has become increasingly applied to medical imaging data, noise in training labels has emerged as an important challenge. Variability in diagnosis of medical ima…
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
Ashish Bora, Siva Balasubramanian, Boris Babenko +13
Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the dev…