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
- Office Location: 2424 Erwin Road, Suite 302, Ravin Advanced Imaging Labs, Durham, NC 27705
- Office Phone: (919) 684-7763
- Email Address: firstname.lastname@example.org
- Duke University, 1995
- Ph.D. Duke University, 1993
- B.S.E.E. Duke University, 1988
- RROMP 301B: Radiology, Radiation Oncology & Medical Physics
In the News
- The First AI Breast Cancer Sleuth That Shows Its Work (Jan 20, 2022 | Pratt School of Engineering)
- 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., Radiology: Artificial Intelligence, 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. American Journal of Roentgenology, 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/j.media.2020.101857] [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., Journal of Medical Imaging (Bellingham, Wash.), 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. American Journal of Roentgenology (2019), pp. 1-7 [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., Journal of Medical Imaging (Bellingham, Wash.), 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].