Overview
My research is at the intersection of computer vision, machine learning, and medical imaging, with a dual focus on mammography and computed tomography (CT). Together with our industry partner, we developed deep learning algorithms for breast cancer screening with 2D/3D mammography, and that product is now undergoing FDA approval with anticipated rollout to clinics worldwide. We also pioneer the creation of "digital twin" anatomical models from patient imaging data, using these models to forge new paths in CT scan analysis through virtual readers and deep learning techniques. Additionally, we're developing a computer-aided triage system for detecting diseases across multiple organs in body CT scans, leveraging hospital-scale datasets and integrating natural language processing with deep learning for comprehensive disease classification.
Current Appointments & Affiliations
Recent Publications
MammoTracker: Mask-Guided Lesion Tracking in Temporal Mammograms
Conference Lecture Notes in Computer Science · January 1, 2026 Accurate lesion tracking in temporal mammograms is essential for monitoring breast cancer progression and facilitating early diagnosis. However, automated lesion correspondence across exams remains a challenges in computer-aided diagnosis (CAD) systems, li ... Full text CiteLarge Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion Models
Conference Lecture Notes in Computer Science · January 1, 2026 Accurate 3D modeling of human organs is critical for constructing digital phantoms in virtual imaging trials. However, organs such as the large intestine remain particularly challenging due to their complex geometry and shape variability. We propose CLAP, ... Full text CiteAAPM task group 234 report: Virtual tools for the evaluation of new 3D/4D breast imaging systems
Journal Article Medical Physics · January 1, 2026 Simulation methods in breast imaging offer advantages over clinical trials in terms of improved reproducibility, reduced need for patient exposure to radiation, increased flexibility, and more clearly defined ground truth. Simulation also allows for improv ... Full text CiteRecent Grants
SCH: Interpretable Machine Learning and Discovery in Medical Images
ResearchCo-Principal Investigator · Awarded by National Institutes of Health · 2025 - 2029Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications
ResearchCo Investigator · Awarded by National Cancer Institute · 2022 - 2027Computer-Aided Triage of Body CT Scans with Deep Learning
ResearchPrincipal Investigator · Awarded by National Cancer Institute · 2025 - 2027View All Grants