Joseph Yuan-Chieh Lo

Professor in Radiology

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.

Appointments and Affiliations

  • Professor in Radiology
  • Professor in the Department of Electrical and Computer Engineering
  • Member of the Duke Cancer Institute

Contact Information

  • Office Location: 2424 Erwin Road, Suite 302, Ravin Advanced Imaging Labs, Durham, NC 27705
  • Office Phone: (919) 684-7763
  • Email Address: joseph.lo@duke.edu
  • Websites:

Education

  • B.S.E.E. Duke University, 1988
  • Ph.D. Duke University, 1993
  • Duke University, 1995

Courses Taught

  • RROMP 301B: Radiology, Radiation Oncology & Medical Physics

In the News

Representative Publications

  • Tushar, Fakrul Islam, Vincent M. D’Anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, and Joseph Y. Lo. “Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.” Radiol Artif Intell 4, no. 1 (January 2022): e210026. https://doi.org/10.1148/ryai.210026.
  • Grimm, Lars J., Benjamin Neely, Rui Hou, Vignesh Selvakumaran, Jay A. Baker, Sora C. Yoon, Sujata V. Ghate, et al. “Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography.” Ajr Am J Roentgenol 216, no. 4 (April 2021): 903–11. https://doi.org/10.2214/AJR.20.23679.
  • Draelos, Rachel Lea, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, and Lawrence Carin. “Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.” Med Image Anal 67 (January 2021): 101857. https://doi.org/10.1016/j.media.2020.101857.
  • Abadi, Ehsan, William P. Segars, Benjamin M. W. Tsui, Paul E. Kinahan, Nick Bottenus, Alejandro F. Frangi, Andrew Maidment, Joseph Lo, and Ehsan Samei. “Virtual clinical trials in medical imaging: a review.” J Med Imaging (Bellingham) 7, no. 4 (July 2020): 042805. https://doi.org/10.1117/1.JMI.7.4.042805.
  • Hou, Rui, Maciej A. Mazurowski, Lars J. Grimm, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, Eun-Sil Shelley Hwang, and Joseph Y. Lo. “Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation.” Ieee Trans Biomed Eng 67, no. 6 (June 2020): 1565–72. https://doi.org/10.1109/TBME.2019.2940195.
  • Georgian-Smith, Dianne, Nancy A. Obuchowski, Joseph Y. Lo, Rachel F. Brem, Jay A. Baker, Paul R. Fisher, Alice Rim, Wei Zhao, Laurie L. Fajardo, and Thomas Mertelmeier. “Can Digital Breast Tomosynthesis Replace Full-Field Digital Mammography? A Multireader, Multicase Study of Wide-Angle Tomosynthesis.” Ajr Am J Roentgenol, April 1, 2019, 1–7. https://doi.org/10.2214/AJR.18.20294.
  • Rossman, Andrea H., Matthew Catenacci, Christine Zhao, Dhiraj Sikaria, John E. Knudsen, Danielle Dawes, Michael E. Gehm, Ehsan Samei, Benjamin J. Wiley, and Joseph Y. Lo. “Three-dimensionally-printed anthropomorphic physical phantom for mammography and digital breast tomosynthesis with custom materials, lesions, and uniform quality control region.” J Med Imaging (Bellingham) 6, no. 2 (April 2019): 021604. https://doi.org/10.1117/1.JMI.6.2.021604.
  • Sturgeon, Gregory M., Subok Park, William Paul Segars, and Joseph Y. Lo. “Synthetic breast phantoms from patient based eigenbreasts.” Med Phys 44, no. 12 (December 2017): 6270–79. https://doi.org/10.1002/mp.12579.
  • Ikejimba, Lynda, Joseph Y. Lo, Yicheng Chen, Nadia Oberhofer, Nooshin Kiarashi, and Ehsan Samei. “A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom.” Med Phys 43, no. 4 (April 2016): 1627. https://doi.org/10.1118/1.4943373.
  • Erickson, David W., Jered R. Wells, Gregory M. Sturgeon, Ehsan Samei, James T. Dobbins, W Paul Segars, and Joseph Y. Lo. “Population of 224 realistic human subject-based computational breast phantoms.” Med Phys 43, no. 1 (January 2016): 23. https://doi.org/10.1118/1.4937597.