Our team is dedicated to developing equitable AI models for disease screening with a special focus on ocular diseases. Recently, we have developed a fair identity normalization technique to improve model performance equity.
Our team is dedicated to developing AI-based data cleaning technologies, aiming to provide cleaner data and enhance diagnostic outcomes for eye diseases.
AI for identifying imaging endophenotypes for GWAS
Our team proposes to uncover the hidden structural patterns of OCT-derived retina layers with two AI models. These patterns are useful for further genetic-wide analysis studies (GWAS).
Our team aims to design a deep learning model to quantify the retinal layer importance and regional importance of each retinal layer in predicting various ocular and systematic diseases.
Our team aim to better characterize the functional loss from ocular diseases with the goals of developing novel diagnostic methods and improving the progression prediction.
We aim to understand the complex inter-relationship between demographics, socioeconomics, and eye disease characteristics by omprehensive statistical analyses.
Our team are participating in a population-based study that includes ocular imaging and a large number of physiological and cognitive parameters to systematically investigate the effects of aging and lifestyle variables on eye diseases.
