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 CiteThe Duke Lung Cancer Screening (DLCS) Dataset: A Reference Dataset of Annotated Low-Dose Screening Thoracic CT.
Journal Article Radiol Artif Intell · July 2025 The Duke Lung Cancer Screening (DLCS) Dataset contains is a large collection of lung cancer screening low-dose CT scans for lung nodule classification with annotations performed in a semi-automated manner, requiring substantially reduced radiologist effort ... Full text Link to item CiteXCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans.
Journal Article Med Image Anal · July 2025 Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current libraries of c ... Full text Link to item 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