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 equalize feature importance between different identity groups 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.
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 aims to better characterize the functional loss from ocular diseases with the goal of developing novel diagnostic methods and improving the prediction of how some diseases may progress in future.
Our team is investigating the relationship between damage of retinal structure and their precise effects on functional vision in eye diseases.
Our team is participating in the analysis work of two large clinical cohort studies. We aim to investigate the interactions of genes, environment, society, and personal lifestyle with disease onset and progression.
Our team is participating in a population-based study that includes ocular imaging and a large number of physiological and cognitive parameters to systematically investigate the relationship between retinal parameters and age, as well as various groups of lifestyle-related variables.