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 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 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.