Joseph Yuan-Chieh Lo

Joseph Yuan-Chieh Lo

Professor in Radiology

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.

Appointments and Affiliations

  • Professor in Radiology
  • Professor in the Department of Electrical and Computer Engineering
  • Professor of Biomedical 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: +1 919 684 7763
  • Email Address:
  • Websites:


  • Duke University, 1995
  • Ph.D. Duke University, 1993

Courses Taught

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

In the News

Representative Publications

  • Tushar, FI; D'Anniballe, VM; Hou, R; Mazurowski, MA; Fu, W; Samei, E; Rubin, GD; Lo, JY, Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning., Radiol Artif Intell, vol 4 no. 1 (2022) [10.1148/ryai.210026] [abs].
  • Grimm, LJ; Neely, B; Hou, R; Selvakumaran, V; Baker, JA; Yoon, SC; Ghate, SV; Walsh, R; Litton, TP; Devalapalli, A; Kim, C; Soo, MS; Hyslop, T; Hwang, ES; Lo, JY, Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography., AJR Am J Roentgenol, vol 216 no. 4 (2021), pp. 903-911 [10.2214/AJR.20.23679] [abs].
  • Draelos, RL; Dov, D; Mazurowski, MA; Lo, JY; Henao, R; Rubin, GD; Carin, L, Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes., Med Image Anal, vol 67 (2021) [10.1016/] [abs].
  • Abadi, E; Segars, WP; Tsui, BMW; Kinahan, PE; Bottenus, N; Frangi, AF; Maidment, A; Lo, J; Samei, E, Virtual clinical trials in medical imaging: a review., J Med Imaging (Bellingham), vol 7 no. 4 (2020) [10.1117/1.JMI.7.4.042805] [abs].
  • Hou, R; Mazurowski, MA; Grimm, LJ; Marks, JR; King, LM; Maley, CC; Hwang, E-SS; Lo, JY, Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation., IEEE Trans Biomed Eng, vol 67 no. 6 (2020), pp. 1565-1572 [10.1109/TBME.2019.2940195] [abs].
  • Georgian-Smith, D; Obuchowski, NA; Lo, JY; Brem, RF; Baker, JA; Fisher, PR; Rim, A; Zhao, W; Fajardo, LL; Mertelmeier, T, Can Digital Breast Tomosynthesis Replace Full-Field Digital Mammography? A Multireader, Multicase Study of Wide-Angle Tomosynthesis., AJR Am J Roentgenol, vol 212 no. 6 (2019), pp. 1393-1399 [10.2214/AJR.18.20294] [abs].
  • Rossman, AH; Catenacci, M; Zhao, C; Sikaria, D; Knudsen, JE; Dawes, D; Gehm, ME; Samei, E; Wiley, BJ; Lo, JY, 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), vol 6 no. 2 (2019) [10.1117/1.JMI.6.2.021604] [abs].
  • Sturgeon, GM; Park, S; Segars, WP; Lo, JY, Synthetic breast phantoms from patient based eigenbreasts., Med Phys, vol 44 no. 12 (2017), pp. 6270-6279 [10.1002/mp.12579] [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., Med Phys, 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., Med Phys, vol 43 no. 1 (2016) [10.1118/1.4937597] [abs].