Joseph Yuan-Chieh Lo

Joseph Yuan-Chieh Lo

Professor of Radiology

My lab focuses on the diagnosis and treatment of breast cancer using advanced imaging techniques. There are 2 main projects: radiomics and breast modeling.

First, radiomics is an interdisciplinary field combining computer vision, machine learning, and informatics. We develop computer vision algorithms to detect suspicious mammographic lesions. We also created predictive models that use machine learning and statistical analysis in order to classify mammograms. In ongoing studies funded by NIH, DOD, and several other organizations, we are addressing the clinically significant challenge of over-diagnosis of DCIS. By exploring the relationship between imaging findings and genomic markers, we seek 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.  

Second, we are designing new virtual breast models that are based on actual patient data. These models go far beyond conventional phantoms in portraying realistic breast anatomy. Furthermore, we can transform these virtual models into physical form using the latest 3D printing technology. Such physical phantoms can be scanned on actual mammography and digital breast tomosynthesis systems, allowing us to assess image quality in new ways that are not only quantitative but also clinically relevant. We continue to refine the realism of these physical phantoms, and seek to develop new procedures for quality control, system evaluation, and the long term goal of virtual clinical trials.

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: joseph.lo@duke.edu
  • Websites:

Education

  • 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)
  • EGR 393: Research Projects in Engineering
  • MEDPHY 751: Seminars in Medical Physics
  • MEDPHY 791: Independent Study in Medical Physics
  • RROMP 301B: RADIOLOGY, RADIATION ONCOLOGY & MEDICAL PHYSICS

Representative Publications

  • 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].
  • Kiarashi, N; Nolte, LW; Lo, JY; Segars, WP; Ghate, SV; Solomon, JB; Samei, E, Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study., Journal of Medical Imaging (Bellingham, Wash.), vol 3 no. 3 (2016) [10.1117/1.JMI.3.3.035504] [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].
  • Kiarashi, N; Nolte, AC; Sturgeon, GM; Segars, WP; Ghate, SV; Nolte, LW; Samei, E; Lo, JY, Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data., Medical Physics, vol 42 no. 7 (2015), pp. 4116-4126 [10.1118/1.4919771] [abs].