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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


XCAT 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 Cite

Improving annotation efficiency for fully labeling a breast mass segmentation dataset.

Journal Article J Med Imaging (Bellingham) · May 2025 PURPOSE: Breast cancer remains a leading cause of death for women. Screening programs are deployed to detect cancer at early stages. One current barrier identified by breast imaging researchers is a shortage of labeled image datasets. Addressing this probl ... Full text Link to item Cite
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Recent Grants


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 · 2023 - 2027

FW-HTF-R: Interpretable Machine Learning for Human-Machine Collaboration in High Stakes Decisions in Mammography

ResearchCo-Principal Investigator · Awarded by National Science Foundation · 2022 - 2026

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Education, Training & Certifications


Duke University · 1993 Ph.D.