Joseph Yuan-Chieh Lo


Professor of Radiology

My research focuses on computer vision and machine learning in medical imaging, with a focus on mammography and CT imaging. There are three specific projects:

First, we seek to address the challenge of overtreatment of DCIS, a type of pre-cancer of the breast. We develop conventional and deep learning algorithms to diagnose mammograms. We also explore the relationship between imaging findings and proteomic/genomic markers. Ultimately, we hope to predict which cases of DCIS are likely to be indolent vs. aggressive, thus providing women with more personalized risk assessment to inform their treatment decisions. This work is funded by NIH, DOD, CRUK, and other agencies.

Second, we design virtual breast models that are based on actual patient data and thus boast highly realistic breast anatomy. Furthermore, we can transform these virtual models into physical form using the latest 3D printing technology. In work funded by NIH, we are translating this work to produce a new generation of realistic phantoms for CT. Such physical phantoms can be scanned on actual imaging devices, allowing us to assess image quality in new ways that are not only quantitative but also clinically relevant.

Third, we are also pursuing an ambitious goal of simultaneously segmenting and classifying multiple diseases in multiple organs from chest-abdomen-pelvis CT scans. The goal is to provide automated labeling of hospital-scale data sets (potentially hundreds of thousands of studies) to produce sufficient data for deep learning studies. This work includes natural language processing to analyze radiology reports, and deep learning models for the segmentation and classification tasks.

Appointments and Affiliations

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

Contact Information

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


  • Duke University, 1995
  • Duke University, 1993
  • Duke University, 1990
  • Ph.D. Duke University, 1993
  • B.S.E.E. Duke University, 1988

Research Interests

Computer vision and machine learning for medical imaging diagnosis; radiogenomics for improved management of breast cancer; computational and physical breast models for virtual clinical trials

Courses Taught

  • BME 394: Projects in Biomedical Engineering (GE)
  • BME 493: Projects in Biomedical Engineering (GE)
  • BME 494: Projects in Biomedical Engineering (GE)
  • MEDPHY 791: Independent Study in Medical Physics
  • RROMP 301B: Radiology, Radiation Oncology & Medical Physics

Representative Publications

  • Shi, B; Grimm, LJ; Mazurowski, MA; Baker, JA; Marks, JR; King, LM; Maley, CC; Hwang, ES; Lo, JY, Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features., Journal of the American College of Radiology : Jacr, vol 15 no. 3 Pt B (2018), pp. 527-534 [10.1016/j.jacr.2017.11.036] [abs].
  • Sturgeon, GM; Park, S; Segars, WP; Lo, JY, Synthetic breast phantoms from patient based eigenbreasts., Medical Physics, vol 44 no. 12 (2017), pp. 6270-6279 [10.1002/mp.12579] [abs].
  • Shi, B; Grimm, LJ; Mazurowski, MA; Baker, JA; Marks, JR; King, LM; Maley, CC; Hwang, ES; Lo, JY, Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?, Academic Radiology, vol 24 no. 9 (2017), pp. 1139-1147 [10.1016/j.acra.2017.03.013] [abs].
  • Ikejimba, LC; Glick, SJ; Choudhury, KR; Samei, E; Lo, JY, Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms., Medical Physics, vol 43 no. 10 (2016) [10.1118/1.4962475] [abs].
  • Ikejimba, L; Lo, JY; Chen, Y; Oberhofer, N; Kiarashi, N; Samei, E, A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom., Medical Physics, vol 43 no. 4 (2016) [10.1118/1.4943373] [abs].
  • Erickson, DW; Wells, JR; Sturgeon, GM; Samei, E; Dobbins, JT; Segars, WP; Lo, JY, Population of 224 realistic human subject-based computational breast phantoms., Medical Physics, vol 43 no. 1 (2016) [10.1118/1.4937597] [abs].