Le Zhang bio photo

Le Zhang

Assistant Professor in Machine Learning/AI for Digital Healthcare, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham.

Honorary Research Fellow at William Harvey Research Institute, Barts Heart Center, Barts Health NHS Trust, Queen Mary University of London.

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About

Dr. Le Zhang is an Assistant Professor at the School of Engineering, College of Engineering and Physical Sciences in the University of Birmingham. He was a Postdoc Researcher at the University of Oxford since 2022. Before that, he was a Research Fellow at University College London since 2019 working with Prof. Daniel Alexander, Prof. Olga Ciccarelli and Prof. Frederik Barkhof. Under the supervision of Prof. Alejandro F Frangi, he obtained his Ph.D. in Medical Image Computing from the University of Sheffield in 2019.

His research is focused on computational modelling, pattern recognition, and machine learning for biomedical imaging and data analysis. His research aims to add understanding and capability in biomedicine by drawing on ideas from medical imaging, computer vision, data science, and machine learning. Much of his work focusses on Cardiac Magnetic Resonance Image Analysis, Multi-Modality Neuroimage Analysis, and Retinal Fundus Image Analysis. His research has been published in top conferences and journals in the fields of machine learning and medical image analysis, including NeurIPS, MICCAI, Medical Image Analysis and Pattern Recognition, and nominated for the MICCAI Young Scientist Award in 2019.

Dr. Le Zhang serves as an Honorary Research Fellow actively associated with the William Harvey Research Institute, Barts Heart Center, Barts Health NHS Trust, Queen Mary University of London. In this role, he collaborates closely with Dr. Nay Aung, Senior Clinical Lecturer and Consultant Cardiologist, and Prof. Steffen Petersen, Professor of Cardiovascular Medicine, to develop explainable machine learning and AI models leveraging the UK Biobank dataset. His work focuses on creating innovative computational approaches to improve the interpretability of AI-driven predictions in cardiovascular imaging, with the aim of enhancing clinical decision-making and advancing personalized medicine in cardiology.

News:

  • March 2025: Our edited book “Generative Machine Learning Models in Medical Image Computing” published by Springer Cham is online.
  • Jan 2025: One paper accepted by ISBI 2025.
  • Oct 2024: A huge congratulations to my first year PhD student Pengyao Qin for securing the 2nd place in the DIAMOND challenge in MICCAI 2024.
  • Aug 2024: One EPSRC funded PhD studentship awarded.
  • June 2024: One EPSRC funded summer project studentship awarded.
  • October 2023: I have been appointed as an Assistant Professor at the University of Birmingham.
  • October 2019: One of my MICCAI 2019 papers has been nominated for the Young Scientist Award.
  • June 2019: Two papers with straight acceptance by MICCAI 2019.

Opening:

Dr. Le Zhang is looking for PhD students and visiting scholars with strong motivation to work on reliable machine learning and medical image analysis, especially PhD students with CSC’s support potentially. Please feel free to drop an email (l.zhang.16@bham.ac.uk) if you are interested!

  • Foundation models for generalizable disease detection from retinal images
  • Generative AI for Medical Imaging
  • Multimodal learning for population health studies
  • Computational imaging methods for population image

Collaboration:

Dr. Zhang has extensive collaborations with universities and industries both domestically and internationally, including University of Oxford, Novo Nordisk, University College London, Johns Hopkins University, Queen Mary University of London, University of Sheffield, Chinese Academy of Sciences University, Alibaba, Tencent, and others.

Selected Publication:

  • Le Zhang, Ryutaro Tanno, Moucheng Xu, Yawen Huang, Kevin Bronik, Chen Jin, Joseph Jacob, Yefeng Zheng, Ling Shao, Olga Ciccarelli, Frederik Barkhof, Daniel C. Alexander, Leaning to Segment from Multiple Annotators, Pattern Recognition, February, 2023.
  • Yan Xia, Le Zhang, Nishant Ravikumar, Rahman Attar, Stefan Piechnik, Stefan Neubauer, Steffen Petersen and Alejandro F. Frangi, Recovering from Missing Data in Population Imaging – Cardiac MR Image Imputation via Conditional Generative Adversarial Nets, Medical Image Analysis, 67 (2021): 101812.
  • Le Zhang, Ryutaro Tanno, Moucheng Xu, Jin Chen, Joseph Jacob, Olga Ciccarelli, Frederik Barkhof, Daniel C. Alexander, Disentangling Human Error from the Ground Truth in Segmentation of Medical Images, Advances in Neural Information Processing Systems (NeurIPS), 2020.
  • Le Zhang, Macro Pereanez, Christopher Bowles, Stefan Piechnik, Stefan Neubauer, Steffen Petersen and Alejandro F. Frangi, Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, Cham, 2019. (Nominated for the Young Scientist Award)