Joseph Y 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 virtual breast models that are based on actual patient data and thus contain highly realistic anatomy. We transform these virtual models into physical form using customized 3D printing technology. With NIH funding, 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.
(3) We develop computer-aided triage tools to classify multiple diseases in chest-abdomen-pelvis CT scans. We are building hospital-scale data sets with 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 of Biomedical Engineering
- 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
My lab investigates three areas in the advanced imaging of breast cancer: (1) digital mammography and breast tomosynthesis, (2) radiogenomics for improved management of breast cancer using computer vision and machine learning models, and (3) computational and physical breast phantoms to facilitate virtual clinical trials of new imaging technology.
Awards, Honors, and Distinctions
- BME 494: Projects in Biomedical Engineering (GE)
- ECE 891: Internship
- RROMP 301B: Radiology, Radiation Oncology & Medical Physics
- 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].
- Tushar, FI; D'Anniballe, VM; Hou, R; Mazurowski, MA; Fu, W; Samei, E; Rubin, GD; Lo, JY, Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans., Corr, vol abs/2008.01158 (2020) [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].