Dr. S. Kevin Zhou is a professor and doctoral supervisor at CAS, and an NAI, IEEE and AIMBE fellow. His research interests are in medical image computing and computer vision. Dr. Zhou has published 200+ peer-reviewed journals, conference and workshop papers. The total number of citations in Google Scholar is over 10,000, and the h-index is 50. He has edited 5 books, among which Deep Learning for Medical Image Analysis has sold more than 1,000 hard copies, and Handbook of Medical Image Computing and Computer Assisted Intervention is an authoritative reference book in the MICCAI research. He has worked in the industry for 14 years and served as the senior R&D director and chief AI expert of Siemens. More than 140 authorized patents have been granted, and his algorithm has been successfully transferred to more than 10 FDA-approved products. The products are used in thousands of hospitals around the world for clinical treatment and diagnosis of more than 7 million patients.

Research projects

1. Intelligent Medical Imaging Equipment, CAS, 2020.01-2022.12, Principal Investigator;

2. Design and Research of New Deep Learning Models for Medical Image Analysis & Medical Image Federated Learning, supported by several companies, 2020.01-2021.12, Principal Investigator.

Awards

1．Fellow of National Academy of Inventors (NAI), 2021.01

2．Fellow of The Institute of Electrical and Electronics Engineers (IEEE), 2020.01

3．Fellow of American Institute for Medical and Biological Engineering (AIMBE), 2016.01

4．R&D 100 Award (Oscar of invention), 2014

5．Siemens Inventor of the Year, 2014

Publications within 2 years

(5 edited books, 200+ peer-reviewed journals, conference and workshop papers)

1．S.  Kevin Zhou, Daniel Rueckert, and Gabor Fichtinger (Eds.) Handbook of Medical Image Computing and Computer Assisted Intervention, Elsevier, 2019.

2．S.  Kevin Zhou, H. Greenspan, C. Davatzikos, J.S. Duncan, B. van Ginneken,  A. Madabhushi, J.L. Prince, D. Rueckert, and R.M. Summers, “A review of  deep learning in medical imaging: Imaging traits, technology trends,  case studies with progress highlights, and future promises,” Proceedings  of the IEEE, 2021. 

3．Q.  Yao, L. Xiao, P. Liu, and S. Kevin Zhou, “Label-free segmentation of COVID-19 lesions in lung CT,” IEEE Trans. on Medical Imaging, 2021.

4．G.  Shi, L. Xiao, Y. Chen, and S. Kevin Zhou, “Marginal loss and exclusion loss for partially supervised multi-organ segmentation,” Medical Image Analysis, 2021. 

5．B.  Zhou, Z. Augenfeld, J. Chapiro, S. Kevin Zhou, C. Liu, and J.S. Duncan, “Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration”, Medical Image Analysis, 2021.

6．B.  Zhou, S. Kevin Zhou, J.S. Duncan, and C. Liu, “Limited view tomographic reconstruction using a cascaded residual dense spatial-channel attention network with projection data fidelity layer”, IEEE Trans. on Medical Imaging, 2021.

7．X.  Wei, Z. Yang, X. Zhang, G. Liao, A. Sheng, S. Kevin Zhou, Y. Wu, L. Du,  “Deep collocative learning for immunofixation electrophoresis image  analysis,” IEEE Trans. on Medical Imaging, 2021.

8．J.  Cai, H. Han, J. Cui, J. Chen, L. Liu, and S. Kevin Zhou,  “Semi-supervised natural face de-occlusion,” IEEE Trans. on Information  Forensics & Security, Vol. 16, pp. 1044-1057, 2020. 

9．J.  Zhu, Y. Li, Y. Hu, K. Ma, S. Kevin Zhou, and Y. Zheng, “Rubik’s cube+: A  self-supervised feature learning framework for 3D medical image  analysis,” Medical Image Analysis, Vol. 64, p101746, 2020. 

10．H.  Li, H. Han, Z. Li, L. Wang, Z. Wu, J. Lu, and S. Kevin Zhou,  “High-resolution chest X-ray bone suppression using unpaired CT  structural priors,” IEEE Trans. on Medical Imaging, Vol. 39, No. 10, pp.  3053-3063, 2020.

11．H.  Liao, W.A. Lin, S. Kevin Zhou, and J. Luo, “ADN: Artifact  disentanglement network for unsupervised metal artifact reduction,” IEEE  Trans. on Medical Imaging, Vol. 39, No. 3, pp. 634-643, 2020.

Granted patents within 2 years (140+ granted patents)

1．Grant US10910099, Segmentation, landmark detection and view classification using multi-task learning. 2021-02-02. 5/5

2．Grant US10878219, Method and system for artificial intelligence based medical image segmentation. 2020- 12-29. 1/14

3．Grant US10779785, Semantic segmentation for cancer detection in digital breast tomosynthesis. 2020-09- 22. 10/10 9. 

4．Grant US10748277, Tissue characterization based on machine learning in medical imaging. 2020-08-18. 1/8

5．Grant US10643105, Intelligent multi-scale medical image landmark detection. 2020-05-05. 8/8 

6．Grant US10627470, System and method for learning based magnetic resonance fingerprinting. 2020-04-21. 4/10 

7．Grant US10607342, Atlas-based contouring of organs at risk for radiation therapy. 2020-03-31. 3/12 

8．Grant US10600185, Automatic liver segmentation using adversarial image-to-image network. 2020-03-24. 3/6 

9．Grant US10582907, Deep learning based bone removal in computed tomography angiography. 2020-03-10. 5/10 

10.  Grant US10565707, 3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes.  2020-02-18. 3/9