Jingchen Ma, PhD

Profile Headshot


Dr. Ma has a solid foundation in physics and mathematics. He is an associate research scientist in the Computational Image Analysis Laboratory, where he dedicates his time to medical image analysis research, which is a multidisciplinary field of radiology, medical physics, artificial intelligence, data science, and statistics. Dr. Ma is developing novel artificial intelligence techniques to improve computer-aided diagnosis methods for medical image analysis as well as cancer diagnosis and assessment. Specifically, he has been focusing on lung and liver nodule analysis and CT quality control of optimal contrast enhancement with radiomics, machine learning, and deep learning approaches. Currently, he is interested in automated universal lesion detection, segmentation, classification, registration, and measurement in full-body CT for cancer screening, diagnosis, and response assessment. Dr. Ma’s goal is to make radiology easier and better for both patients and radiologists!

Academic Appointments

  • Associate Research Scientist in the Department of Radiology


  • Male

Credentials & Experience

Education & Training

  • BS, Zhiyuan College, Shanghai Jiao Tong University
  • PhD, Shanghai Jiao Tong University


Selected Publications

1. Ma J, Dercle L, Lichtenstein P, Wang D, Chen A, Zhu J, Piessevaux H, Zhao J, Schwartz LH, Lu L. Automated identification of optimal portal venous phase timing with convolutional neural networks. Academic radiology 2020;27(2):e10-e18.

2. Dercle L, Ma J, Xie C, Chen A-p, Wang D, Luk L, Revel-Mouroz P, Otal P, Peron J-M, Rousseau H. Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine. European journal of radiology 2020;125:108850.

3. Jia T-Y, Xiong J-F, Li X-Y, Yu W, Xu Z-Y, Cai X-W, Ma J-C, Ren Y-C, Larsson R, Zhang J. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. European radiology 2019;29(9):4742-4750.

4. Xiong J, Yu W, Ma J, Ren Y, Fu X, Zhao J. The role of PET-based radiomic features in predicting local control of esophageal cancer treated with concurrent chemoradiotherapy. Scientific reports 2018;8(1):1-11.

5. Ren Y, Ma J, Xiong J, Chen Y, Lu L, Zhao J. Improved false positive reduction by novel morphological features for computer-aided polyp detection in CT colonography. IEEE journal of biomedical and health informatics 2018;23(1):324-333.

6. Ma J, Zhou Z, Ren Y, Xiong J, Fu L, Wang Q, Zhao J. Computerized detection of lung nodules through radiomics. Medical physics 2017;44(8):4148-4158.

7. Xiong J, Shao Y, Ma J, Ren Y, Wang Q, Zhao J. Lung field segmentation using weighted sparse shape composition with robust initialization. Medical physics 2017;44(11):5916-5929.

8. Fu L, Ma J, Ren Y, Han YS, Zhao J. Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features.  Medical Imaging 2017: Computer-Aided Diagnosis: International Society for Optics and Photonics, 2017; p. 101340A.

9. Ren Y, Ma J, Xiong J, Lu L, Zhao J. High-performance CAD-CTC scheme using shape index, multiscale enhancement filters, and radiomic features. IEEE Transactions on Biomedical Engineering 2016;64(8):1924-1934.

10. Ma J, Wang Q, Ren Y, Hu H, Zhao J. Automatic lung nodule classification with radiomics approach.  Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations: International Society for Optics and Photonics, 2016; p. 978906.

For a full list of Dr. Ma's publications, visit Google Scholar