Yunan Wu

This paper develops a two-stage deep learning model to reduce motion artifacts in liver DCE-MRI, achieving better results than existing methods.

This paper introduces geometric deep learning frameworks for analyzing brain morphology in Alzheimer's disease, showing improved classification and morphological insights compared to prior methods.

This paper proposes an attention-based multiple instance learning approach for CT hemorrhage detection using scan-level labels, achieving better performance than methods relying on slice-level annotations.

This study uses geometric deep learning to predict fluid intelligence from brain morphology across two cohorts, finding that amygdala, hippocampus, and specific cortical regions are key predictors.