I am a Lecturer in Computer Vision and Machine Learning at Department of Electrical and Electronic Engineering and I-X, Imperial College London. Previously, I was a Lecturer at School of Engineering, University of Edinburgh from July 2020 to September 2022. I obtained my Ph.D. in Computing Research from Imperial College London in January 2020, and M.Sc. in Control Science and Engineering from Department of Automation, Tsinghua University in July 2015. Before moving to University of Edinburgh, I have worked as a post-doctoral research associate at Department of Computing, Imperial College London, where I have worked on deep learning-based MRI reconstruction, image registration and segmentation. Our pioneering work on convolutional recurrent neural networks for MRI reconstruction has been highly and widely recognised by international leading groups from both academia and industry. Overall, we have published more than 50 papers in top-tier peer-reviewed engineering and medical imaging journals and conference proceedings. I have also served as an area chair for MICCAI 2022/23 and a member of organising and programme committee at several international workshops, e.g., CMRxMotion 2022 and MLMIR 2022.
Machine Learning, Deep Learning, Medical Image Analysis, Computer Vision
My research is interdisciplinary in nature and at the intersection between machine learning and medical imaging, with a vision towards improving medical imaging workflow via machine intelligence for significant impact in clinical use. Our current research mainly focuses on the development of effective and robust machine learning algorithms for medical image computing and analysis, such as MR image reconstruction, medical image segmentation, image registration and motion tracking. We are currently mainly working on clinical applications of medical image computing in neurology and cardiology.
We are also part of the Biomedical Image Analysis Group.
I am looking for highly motivated PhD students to work on machine learning for medical image computing at Imperial College London. Prospective students will have a good first degree in Computer Science, Electrical Engineering, Automation or other engineering-related disciplines. Please feel free to drop me an email if you’re interested.
I am looking for two research assistants/associates on machine learning in medical imaging. For details, please check the Vacancies page.
[07/2023] Dr Chen Qin is awarded the EPSRC New Investigator Award for the project “TrustMRI: Trustworthy and Robust Magnetic Resonance Image Reconstruction with Uncertainty Modelling and Deep Learning”.
[06/2023] Dr Chen Qin is awarded the EPSRC Early Career Researcher International Collaboration Grants for the project “Towards Motion-Robust and Efficient Functional MRI Using Implicit Function Learning”.
[05/2023] Call for participation for our MICCAI challenge on “Cardiac MRI Reconstruction” to be held in conjunction with MICCAI 2023
[03/2023] Call for papers for our special issues on “The role of artificial intelligence in MRI/MRS acquisition and reconstruction” in Magnetic Resonance Materials in Physics, Biology and Medicine
[02/2023] One paper accepted at IEEE Trasactions on Computational Imaging
[01/2023] One paper accepted at IEEE International Symposium on Biomedical Imaging (ISBI), 2023
[01/2023] One paper accepted at Medical Image Analysis (led by Yantai University and Fudan University), 2023
[12/2022] Call for papers for our research topic on “The Combination of Data-Driven Machine Learning Approaches and Prior Knowledge for Robust Medical Image Processing and Analysis” in Frontiers in Medicine
[12/2022] One paper accepted at Medical Image Analysis, 2023
[09/2022] I am joining Department of Electrical and Electronic Engineering and I-X, Imperial College London
Email: c dot qin15 at imperial dot ac dot uk
Address: Translation & Innovation Hub (I-Hub), White City Campus, Imperial College London, W12 7TA