Shuo Li, PhD

Biomedical Engineering
Case School of Engineering, School of Medicine
Computer Data Science
Case School of Engineering
Cancer Imaging Program
Case Comprehensive Cancer Center

Research Information

Research Interests

Dr. Li is a global leader in conducting multi-disciplinary research to enable artificial intelligence (AI) in clinical imaging. He is an academic AI scientist with a background in machine learning, medical data analytics, and imaging. His current research focuses on the development of image-centered AI systems. These systems are designed to solve the most challenging clinical and fundamental data analytics problems in various fields, including cardiology, radiology, urology, surgery, rehabilitation, and cancer. He emphasizes innovations in multiple learning schemes such as deep, regression, reinforcement, adversarial, sparse, spectral, and manifold learning. Dr. Li serves as a committee member for multiple highly influential conferences and societies. He is most notable for his role on the prestigious board of directors of the MICCAI Society, a position he held from 2015 to 2024, and as the general chair of the MICCAI 2022 conference. With over 200 publications, Dr. Li has also acted as the editor for six Springer books and is an associate editor for several prestigious journals. He has received numerous awards from GE, various institutes, and international organizations throughout his career.

Learn more about Shuo Li at Digital Imaging Group of London.


  • Towards Accurate and Robust Domain Adaptation Under Multiple Noisy Environments. R Zhao, X Chen, Z Chen, S Li. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
  • United Adversarial Learning for Liver Tumor Segmentation and Detection of Multi-modality Non-contrast MRI. J Zhao, D Li, X Xiao, F Accorsi, H Marshall, T Cossetto, D Kim, ... Medical Image Analysis 73, 102154, 2022
  • Evaluation and Comparison of Accurate Automated Spinal Curvature Estimation Algorithms with Spinal Anterior-posterior X-Ray Images: The AASCE2019 Challenge. L Wang, C Xie, Y Lin, HY Zhou, K Chen, D Cheng, F Dubost, B Collery, ... Medical Image Analysis, 2022

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