Dr Chen Qin

Lecturer in Computer Vision and Machine Learning



1.10 Alexander Graham Bell building

Personal Page: 

Engineering Discipline: 

  • Electronics and Electrical Engineering

Research Institute: 

  • Digital Communications

Research Theme: 

  • Signal and Image Processing


Dr. Chen Qin is a Lecturer in Computer Vision and Machine Learning at Electronics and Electrical Engineering, The University of Edinburgh. Before that, she worked as a Research Associate at Department of Computing, Imperial College London. She obtained her Ph.D. in Computing Research from Imperial College London in January 2020, M.Sc. in Control Science and Engineering from Tsinghua University in July 2015, and B.Eng. in Automation from Harbin Institute of Technology in July 2012. From July 2018 to May 2019, she also worked at Siemens Healthineers (NJ, USA) and Huawei Technology (London, UK) as a research intern.

Her research is at the interdisciplinary field of artificial intelligence and medical imaging, aiming to improve the entire medical imaging/radiology workflow with significant impact for clinical use via machine intelligence. Her current research mainly focuses on the development of machine learning algorithms for magnetic resonance image reconstruction and analysis, including dynamic MR image reconstruction, medical image registration and segmentation.

Academic Qualifications: 

  • Doctor of Philosophy (PhD), Department of Computing, Imperial College London, 2020
  • Master of Science (MSc), Department of Automation, Tsinghua University, 2015
  • Bachelor of Engineering (BEng), Department of Automation, Harbin Institute of Technology, 2012

Professional Qualifications and Memberships: 

  • Member, The Medical Image Computing and Computer Assisted Intervention Society
  • Member, International Society for Magnetic Resonance in Medicine

Research Interests: 

My research interests are in the areas of machine learning, deep learning, medical image computing and analysis, and computer vision. These include but are not limited to the following topics:

  • Deep learning for inverse problems: MR image reconstruction, image enhancement
  • Correspondence problem: motion tracking, medical image registration
  • Semantic image interpretation: anatomy/lesion detection and segmentation
  • Beyong supervised learning: self-supervised/unsupervised learning, few-shot learning
  • Reinforcement learning
  • Sequential data processing and analysis
  • Model generalisation and domain adaptation
  • Cardiac/Brain Imaging


  • Machine learning/deep learning
  • MR image reconstruction
  • Medical image analysis and interpretation

Further Information: 


I am looking for highly motivated PhD students to work on machine learning for medical image computing in early 2021. Please feel free to drop me an email if you're interested.