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Joseph Yuan-Chieh Lo

Professor in Radiology
Radiology
2424 Erwin Road, Suite 302, Ravin Advanced Imaging Labs, Durham, NC 27705
2424 Erwin Road, Suite 302, Ravin Advanced Imaging Labs, Durham, NC 27705

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


Professor in Radiology · 2021 - Present Radiology, Clinical Science Departments
Professor of Biomedical Engineering · 2014 - Present Biomedical Engineering, Pratt School of Engineering
Professor in the Department of Electrical and Computer Engineering · 2019 - Present Electrical and Computer Engineering, Pratt School of Engineering
Member of the Duke Cancer Institute · 1993 - Present Duke Cancer Institute, Institutes and Centers

In the News


Published January 20, 2022
The First AI Breast Cancer Sleuth That Shows Its Work

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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 Cite

Large 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 Cite

AAPM 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 Cite
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Recent Grants


SCH: Interpretable Machine Learning and Discovery in Medical Images

ResearchCo-Principal Investigator · Awarded by National Institutes of Health · 2025 - 2029

Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications

ResearchCo Investigator · Awarded by National Cancer Institute · 2022 - 2027

Computer-Aided Triage of Body CT Scans with Deep Learning

ResearchPrincipal Investigator · Awarded by National Cancer Institute · 2025 - 2027

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Education, Training & Certifications


Duke University · 1993 Ph.D.