DrChen Qin
Associate Professor
Department of Electrical and Electronic Engineering - Faculty of Engineering
Orcid identifier0000-0003-3417-3092 (opens in a new tab)
- Associate ProfessorDepartment of Electrical and Electronic Engineering - Faculty of Engineering
- Translation & Innovation Hub Building, White City Campus, United Kingdom
RESEARCH
Overview
Dr Qin's 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. Her current research mainly focuses on the development of effective and trustworthy machine learning algorithms for medical image computing and analysis, such as MR image reconstruction, medical image segmentation, image registration and motion correction, leveraging multiple imaging and non-imaging modalities. She is currently working on clinical applications of medical image computing in neurology and cardiovascular.
Her group is also part of the Biomedical Image Analysis Group (https://biomedia.doc.ic.ac.uk/).
Guest Lectures
AI-Driven CMR, at ISMRM Educational Program on Advanced Cardiovascular MRI Techniques, Singapore, May 2024
Deep Learning for Fast MR Imaging and Analysis, at Fast Machine Learning for Science Workshop, London, UK, Sep 2023
Artificial Intelligence Meets Medical Imaging, at Imperial MedTech Links, London, UK, July 2023
Basic Principles of Fast MRI with AI-Based Acquisition & Reconstruction, at ISMRM Educational Session on Artificial Intelligence in Musculoskeletal MRI, Toronto, Canada, June 2023
Deep CMR: Getting it to Work, at ISMRM Member-initiated Tutorials on The Cardiac MRI Rodeo: Taming AI for Clinical Practice, Toronto,
Canada, June 2023
The End of Drawing Circles - Automatic Segmentation, at SCMR Annual Scientific Sessions, San Diego, California, USA, Jan 2023
Deep Learning-Based Image Reconstruction in Cardiac Magnetic Resonance, at Workshop in “Inverse Problems Methods, Applications, and Synergies”, Online, Jan 2023
Data-driven Image Reconstruction, at ISMRM Educational Sessions Advanced Methods for Cardiovascular MRI, London, UK, May 2022
Deep Learning Meets Medical Imaging: From Signals to Clinically Useful Information, at Data Science and Computational Statistics Seminar, University of Birmingham, Online, Nov 2021
Deep Learning for Dynamic MRI Reconstruction, at Artificial Intelligence in NMR, MRI and Neuroscience, Keynote, Online, Feb 2021
MR Image Reconstruction: How Deep Learning Will Shape the Future of MRI, at 11th Annual Scientific Symposium on Ultrahigh Field Magnetic Resonance, Online, Sep 2020
Hands-on Deep Learning, at ISMRM annual meeting educational course, Online, Aug 2020
Machine Learning for Magnetic Resonance Image Reconstruction and Analysis, at Medical Imaging Computing Society, Online, Aug 2020
Dr Qin's 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. Her current research mainly focuses on the development of effective and trustworthy machine learning algorithms for medical image computing and analysis, such as MR image reconstruction, medical image segmentation, image registration and motion correction, leveraging multiple imaging and non-imaging modalities. She is currently working on clinical applications of medical image computing in neurology and cardiovascular.
Her group is also part of the Biomedical Image Analysis Group (https://biomedia.doc.ic.ac.uk/).
Guest Lectures
AI-Driven CMR, at ISMRM Educational Program on Advanced Cardiovascular MRI Techniques, Singapore, May 2024
Deep Learning for Fast MR Imaging and Analysis, at Fast Machine Learning for Science Workshop, London, UK, Sep 2023
Artificial Intelligence Meets Medical Imaging, at Imperial MedTech Links, London, UK, July 2023
Basic Principles of Fast MRI with AI-Based Acquisition & Reconstruction, at ISMRM Educational Session on Artificial Intelligence in Musculoskeletal MRI, Toronto, Canada, June 2023
Deep CMR: Getting it to Work, at ISMRM Member-initiated Tutorials on The Cardiac MRI Rodeo: Taming AI for Clinical Practice, Toronto,
Canada, June 2023
The End of Drawing Circles - Automatic Segmentation, at SCMR Annual Scientific Sessions, San Diego, California, USA, Jan 2023
Deep Learning-Based Image Reconstruction in Cardiac Magnetic Resonance, at Workshop in “Inverse Problems Methods, Applications, and Synergies”, Online, Jan 2023
Data-driven Image Reconstruction, at ISMRM Educational Sessions Advanced Methods for Cardiovascular MRI, London, UK, May 2022
Deep Learning Meets Medical Imaging: From Signals to Clinically Useful Information, at Data Science and Computational Statistics Seminar, University of Birmingham, Online, Nov 2021
Deep Learning for Dynamic MRI Reconstruction, at Artificial Intelligence in NMR, MRI and Neuroscience, Keynote, Online, Feb 2021
MR Image Reconstruction: How Deep Learning Will Shape the Future of MRI, at 11th Annual Scientific Symposium on Ultrahigh Field Magnetic Resonance, Online, Sep 2020
Hands-on Deep Learning, at ISMRM annual meeting educational course, Online, Aug 2020
Machine Learning for Magnetic Resonance Image Reconstruction and Analysis, at Medical Imaging Computing Society, Online, Aug 2020
GRANTS
- STANDARD - RESPONSEPA5592: TrustMRI: Trustworthy and Robust Magnetic Resonance Image Reconstruction with Uncertainty Modelling and Deep LearningEngineering & Physical Science Research Council (E1 Mar 2024 - 31 Aug 2027
- STANDARD - CALLPA6551: Towards Motion-Robust and Efficient Functional MRI Using Implicit Function LearningEngineering & Physical Science Research Council (E1 Mar 2024 - 8 Nov 2025