The overarching goal of our lab is to establish robust, generalizable, and fair artificial intelligence (AI) methods for quantitative pathology evaluation. We developed the first fully-automated algorithm to analyze digital whole-slide pathology images. Our methods successfully identified cancer diagnoses, predicted molecular subtypes, and informed patients' prognoses.
We designed data-driven methods to connect molecular profiles and microscopic imaging patterns of cancer tissues. Our approaches elucidate the molecular aberrations underpinning the diverse morphology of cancer cells.
We develop multi-modal AI models to integrate pathology, molecular, and clinical profiles of patients from diverse populations. Our approaches predicted patients' treatment responses and adverse effects, which informs clinical decisions on treatment selection.
