Abstract
This presentation introduces RETFound, a foundation model trained on over 1.6 million unlabelled retinal images using self-supervised learning. RETFound enables generalizable detection of both ocular and systemic diseases—such as diabetic retinopathy, glaucoma, myocardial infarction, and Parkinson’s disease—with significantly less labeled data. RETFound consistently outperforms supervised baselines and achieves high performance and label efficiency, offering scalable potential for real-world clinical AI deployment.
Pegah Khorasani
Graduate Research Associate
As a doctoral candidate at Arizona State University (ASU), I am currently conducting research under the guidance of Professor Hassan Ghasemzadeh at the Embedded Machine Intelligence Lab (EMIL). My research interests encompass a range of topics, including machine learning, health monitoring system development, and mobile health.