Research

We investigate coronary image analysis, pathology-aware representations, and their relationship to cardiovascular risk.

We study how a model’s image evidence can be exposed, interpreted, and evaluated alongside its predictions.

We investigate collaborative model training when medical images remain at their originating institutions.

We study image analysis for radiation oncology, including target segmentation and the interpretation of imaging changes after treatment.

We investigate how AI image analysis and clinical expertise can support the interpretation of prostate MRI.

We investigate quantitative CT analysis of pulmonary disease, including imaging patterns associated with fibrosis after COVID-19.

We study liver segmentation and MRI representations of cirrhosis, with attention to altered morphology and complementary imaging sequences.

We develop image analysis methods to delineate pancreatic structures and investigate the imaging features associated with pancreatic cyst risk.

We investigate how clinicians’ visual attention can inform image analysis, model training, and human–AI interaction.