University of Cambridge

The University of Cambridge, based in Cambridge, UK, has been a leading academic institution for over eight centuries. The University’s mission is to contribute to society through the pursuit of education, learning and research at the highest international levels of excellence. The University proudly hosts over 20,000 students from 140 countries who are spread across 31 Colleges, 6 Schools and over 150 Faculties and Departments.

Role in the project

The University of Cambridge is involved in the Work Package 2 of ODELIA. This involves the creation of a local breast MR dataset from which to train local AI models to test against the swarm model. Researchers in Cambridge’s Department of Radiology have experience in curating and working with large imaging databases and, in recent years, this has been applied to use with AI systems. The department’s integration with Cambridge University Hospitals allows it to leverage the expertise in high quality MR imaging and highly skilled radiologists.


Fiona Gilbert

Prof Fiona Gilbert is Chair of Radiology and head of department at the University of Cambridge. As well as being an internationally respected radiologist and researcher, she has held various positions – Chair of the Academic committee of the Royal College of Radiologists, Chair of the NCRI Imaging Advisory group, Chair of the Royal College of Radiologists Breast Group, and co-chair of the NCRI PET Research Development group. She is past President of the European Society of Breast Imaging and past chair of the breast subcommittee of Radiological Society of North America. She now sits on the Research & development committee of RSNA.

Her research interests cover imaging technology evaluation, risk-adaptive screening, and the use of artificial intelligence to improve the early detection of cancer.


Nicholas Payne

Dr Nicholas Payne is a research associate with a Ph.D. in medical physics and a background in fast field-cycling MRI. He joined the University of Cambridge in 2019 and has been involved in breast cancer screening research covering supplement imaging, risk prediction, and the use of AI algorithms.