Faculty & Staff
Joseph Y. Lo, Ph.D.
Professor in Radiology
Associate Director, Medical Physics Graduate Program
About
Department/Division
Radiology
Current Appointments & Affiliations
Professor in Radiology, Radiology 2021
Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering 2019
Member of the Duke Cancer Institute, Duke Cancer Institute 1993
Background
Education, Training, & Certifications
B.S.E.E. 1988, Duke University1988
Ph.D. 1993, Duke University1993
Research Associate, Radiology, Duke University1993 - 1995
- PhD, Duke University, 1993
- BSEE, Duke University, 1988
Duke Appointment History
Professor of Radiology2018 - 2020
Professor of Radiology2014 - 2018
Assistant Research Professor in Biomedical Engineering2003 - 2005
Assistant Research Professor in Radiology1995 - 2006
Professor of Biomedical Engineering2014 - 2022
Associate Research Professor in the Department of Electrical and Computer Engineering2011 - 2015
Professor in the Department of Electrical and Computer Engineering2014 - 2016
Associate Professor in the Department of Radiology2009 - 2014
Professor in the Department of Electrical and Computer Engineering2014 - 2019
Assistant Professor in Radiology2006 - 2009
Assistant Professor of Biomedical Engineering2005 - 2014
Assistant Research Professor in Biomedical Engineering1997 - 1999
Interests & Expertise
Overview
My research uses computer vision and machine learning to improve medical imaging, focusing on breast and CT imaging. There are three specific projects:
(1) We design deep learning models to diagnose breast cancer from mammograms. We perform single-shot lesion detection, multi-task segmentation/classification, and image synthesis. Our goal is to improve radiologist diagnostic performance and empower patients to make personalized treatment decisions. This work is funded by NIH, Dept of Defense, Cancer Research UK, and other agencies.
(2) We create "digital twin" anatomical models that are based on actual patient data and thus contain highly realistic anatomy. With customized 3D printing, these virtual phantoms can also be rendered into physical form to be scanned on actual imaging devices, which allows us to assess image quality in new ways that are clinically relevant.
(3) We are building a computer-aided triage platform to classify multiple diseases across multiple organs in chest-abdomen-pelvis CT scans. Our hospital-scale data sets have hundreds of thousands of patients. This work includes natural language processing to analyze radiology reports as well as deep learning models for organ segmentation and disease classification.