Le Zhang bio photo

Le Zhang

I'm from a small town near Wuhan, China. Currently in Birmingham, United Kingdom. Passionate about medical image computing, machine learning, travelling, and cooking.

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About

Dr. Le Zhang is an Assistant Professor in School of Engineering, College of Engineering and Physical Sciences at the University of Birmingham, affiliated with Digital Healthcare and Medical Imaging Research Group. Dr. Zhang 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. Under the supervision of Prof. Alejandro F Frangi, he obtained his Ph.D. in Medical Image Computing from the University of Sheffield in 2019.

News:

  • October 2023: I have been appointed as an Assistant Professor at the University of Birmingham.
  • Feburary 2023: One journal paper accepted to Pattern Recognition.
  • December 2022: One journal paper accepted to Pattern Recognition.
  • June 2022: New job at the Big Data Institue, University of Oxford.
  • January 2021: The extended version of our MICCAI 2019 paper on missing slice imputation has been published by MedIA.
  • September 2020: One paper accepted to NeurIPS 2020.
  • June 2020: One paper accepted to MICCAI 2020.
  • 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.
  • May 2019: I have moved to London and work as a postdoctral research fellow at University College London.
  • November 2018: One journal paper accepted by IEEE Transactions on Biomedical Engineering.
  • June 2018: One paper accepted to MICCAI 2018.

Opening:

Dr. Le Zhang is looking for PhD students and machine learning software engineer 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

Research:

His research is focusing 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 Patten Recognition, and nominated for the MICCAI Young Scientist Award in 2019.

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)