Binsheng Zhao, DSc

Profile Headshot


Binsheng Zhao is a Professor of Radiology (Physics) and the director of the Computational Image Analysis Laboratory in the Department of Radiology. She received her BS and MS degrees in electronic engineering from National Institute of Technology at Changsha, China, and her DSc degree in medical informatics from University of Heidelberg, Germany.

Her areas of research include computer-aided cancer detection and diagnosis; tumor, organ and tissue segmentation; quantitative imaging biomarkers / radiomics for response prediction and assessment in oncology and neurologic diseases; reproducibility of quantitative imaging features; optimized workflow in response assessment .

Academic Appointments

  • Professor of Radiology (Physics)

Administrative Titles

  • Director, Computational Image Analysis Laboratory


  • Chinese
  • German


  • Female

Credentials & Experience

Education & Training

  • BS, 1984 Electrical Engineering, National Institute of Technology at Changsha
  • MS, 1987 Electrical Engineering, National Institute of Technology at Changsha
  • DSc, 1994 Medical Informatics, University of Heidelberg

Honors & Awards

  • 1991: Gottlieb Daimler-Karl Benz Foundation Fellowship, Ladenburg, Germany
  • 1997: President's Council of Cornell Women Award, Ithaca, New York
  • 2014: Award for Recognition of Outstanding Contribution, Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA)


Selected Publications

Technology Development

  • Guo X, Schwartz LH, and Zhao B. Automatic Liver Segmentation by Integrating Fully Convolutional Networks into Active Contour Models. Med Phys. (Accepted)
  • Xiong J, Li X, Lu L, Schwartz LH, Fu X, Zhao J, Zhao B. Implementation Strategy of a CNN Model Affects the Performance of CT Assessment of EGFR Mutation Status in Lung Cancer Patients. IEEE Access. 2019. 7:64583-91.
  • Ma J, Dercle L, Lichtenstein P, Wang D, Chen A, Zhu J, Yang H, Piessevaux H, Zhao J, Schwartz LH, Lu L, Zhao B. Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks. Acad Radiol 2019 May 28. pii: S1076-6332(19)30171-0.
  • Tan Y, L Lu, Bonde A, Wang D, Qi J, Schwartz HL, and Zhao B. Lymph node segmentation by dynamic programming and active contours. Medical Physics 2018; 45(5):2054-2062.
  • Yang H, Schwartz LH, and Zhao B. A Response Assessment Platform for Development and Validation of Imaging Biomarkers in Oncology. Tomography. 2016; 2(4):406-410.
  • Lu L, Tan Y, Schwartz LH and Zhao B. Hybrid detection of lung nodules on CT scan images. Med Phys. 2015 Sep;42(9):5042-54. (Study interviewed and reported in October 2015)
  • Yan J, Schwartz LH, and Zhao B. Semi-automatic segmentation of liver metastases onvolumetric CT images. Med Phys. 2015 Nov;42(11):6283. (Article chosen as 2015 Editor's Picks)
  • Tan Y, Schwartz LH and Zhao B, Segmentation of lung tumors on CT Scans using Watershed and Active Contours. Med Phys. 2013; 40(4):043502.
  • Guo X, Schwartz LH and Zhao B, Semi-automated Segmentation of Multimodal Brain Tumor Using Active Contours. Proceedings of MICCAI 2013, BRATS: 17-30.
  • Cui Y, Tan, Y, Zhao B, Liberman L, Parbhu R, Kaplan J, Theodoulou M, Hudis C, Schwartz L. Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed. Medical Physics 2009; 36:4359-4369.

Reproducibility and Proof-of-Concept Clinical Studies

  • Mokrane F, Lu L, Vavasseur A, Otal P, Peron J, Yang H, Rousseau H, Zhao B, Schwartz LH, Dercle L. Diagnosis of hepatocellular carcinoma in high-risk patients with indeterminate liver nodules using Radiomics: A Proof of Concept Biomarker Development. European radiology. 2019 (Accepted)
  • E L, Lu L, Li L, Yang H, Schwartz LH, Zhao B. Radiomics for Classifying Histological Subtypes of Lung Cancer based on Multiphasic Contrast-Enhanced Computed Tomography. J Comput Assist Tomogr. 2019. 43(2): p. 300-306.
  • Dercle L, Connors DE, Tang Y, Adam SJ, Gönen M, Hilden P, Karovic S, Maitland M, Moskowitz CS, Kelloff G, Zhao B, Oxnard GR, Schwartz LH. Vol-PACT: An FNIH public-private partnership supporting sharing of clinical trial data for development of improved imaging biomarkers in oncology. JCO Clin Cancer Inform. 2018 Dec; 2:1-12. PMID: 30559455
  • Li Y, Lu L, Xiao M, Dercle L, Huang Y, Zhang Z, Schwartz LH, Li D, and Zhao B. CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study. Sci Rep. 2018 Dec 17;8(1):17913. PubMed PMID: 30559455
  • Aerts H, Grossmann P, Tan Y, Oxnard G, Schwartz LH, Zhao B. Defining a RadiomicResponse Phenotype: A Pilot Study using TKI therapy in NSCLC. Nat Sci Rep 6; 33860, 2016.
  • Zhao B, Tan, Y, Tsai W-Y, Qi J, Xie C, Lu L, Schwartz LH. Reproducibility ofradiomics for deciphering tumor phenotype with imaging. Nat Sci Rep 6; 23428, 2016.doi:10.1038/srep23428.
  • Zhao B, Lee S, Lee HJ, Tan Y, Qi J, Persigehl T, Mozley PD and Schwartz LH.Variability in assessing treatment response: metastatic colorectal cancer as a paradigm.Clin Cancer Res. 2014; 20(13):3560-8. (Article featured in Highlights of This Issue).
  • Cohen A, Dempster DW, Recker RR, Lappe JM, Zhou H, Wirth AJ, van Lenthe GH,Zwahlen A, Müller R, Zhao B, Guo X, Lang T, Saeed I, Liu XS, Guo XE, Cremers S, Rosen CJ, Stein EM, Nickolas TL, McMahon DJ, Young P, Shane E. Abdominal fat is associated with lower bone formation and inferior bone quality in healthy premenopausal women: a transiliac bone biopsy study. Clin. Endocrinol. Metab. 2013; 98(6):2562-72. (Study interviewed and reported by Prevention Magazine).
  • Oxnard GR, Zhao B, Sima CS, Ginsberg MS, James LP, Lefkowitz RA, Guo P, Kris MG, Schwartz LH and Riely GJ. Variability of lung tumor measurements on repeat computed tomography (CT) scans taken within 15 minutes: implications for care and clinical research. J Clin Oncol. 2011; 29(23):3114-9. (Article highlighted in an accompanying editorial).
  • Zhao B, Oxnard GR, Moskowitz CS, Kris MG, Pao W, Guo P, Rusch VM, Ladanyi M, Rizvi NA, Schwartz LH. A pilot study of volume measurement as a method of tumor response evaluation to aid biomarker development. Clin Cancer Res 2010; 16:4647-53.(Article highlighted in an accompanying commentary).
  • Zhao B, James LP, Moskowitz C, Guo P, Ginsberg MS, Lefkowitz RA, Qin Y, Riely GJ, Kris MG, Schwartz LH. Evaluating variability in tumor measurements from same-day repeat CT scans in patients with non-small cell lung cancer. Radiology 2009; 252:263-272. (RADIOLOGY Select Podcast Interview)