Scholar
Le Zhang
Google Scholar ID: N3gALRkAAAAJ
Assistant Professor, University of Birmingham
Citations & Impact
Citations
527
H-index
9
i10-index
9
Publications
20
Co-authors
17
list available
Publications
Resume (English only)
Academic Achievements
- Published papers in top conferences and journals such as NeurIPS, MICCAI, Medical Image Analysis, and Pattern Recognition; Nominated for the MICCAI Young Scientist Award in 2019; Served as a guest editor for the special issue 'Computer Vision for Medical Image Analysis' at the Journal of Imaging; Edited book 'Generative Machine Learning Models in Medical Image Computing' published by Springer Cham.
Research Experience
- Assistant Professor at the 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. 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.
Education
- Ph.D. in Medical Image Computing from the University of Sheffield in 2019, supervised by Prof. Alejandro F Frangi; Postdoc Researcher at the University of Oxford since 2022; Research Fellow at University College London since 2019, working with Prof. Daniel Alexander, Prof. Olga Ciccarelli, and Prof. Frederik Barkhof.
Background
- Research interests: computational modeling, 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 focuses on Cardiac Magnetic Resonance Image Analysis, Multi-Modality Neuroimage Analysis, and Retinal Fundus Image Analysis.
Miscellany
- Looking for PhD students, Research Master students (project only), and visiting scholars with strong motivation to work on reliable machine learning and medical image analysis, especially PhD students with CSC’s support potentially.
