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Cambridge Image Analysis

Professor Michael Roberts

Mike Roberts is Research Professor at DAMTP and also at the Department of Medicine. He is a member of the Cambridge Image Analysis group (CIA), leads the BloodCounts! consortium (https://www.bloodcounts.org/) and led the algorithm development team for the global COVID-19 AIX-COVNET collaboration (https://covid19ai.maths.cam.ac.uk/).

Career

Positions:

October 2023 onwards: Research Professor at DAMTP and Department of Medicine, University of Cambridge, UK.

April 2021 to September 2023: Senior Research Associate at DAMTP, University of Cambridge, UK.

March 2020 to March 2021: Research Associate at DAMTP, University of Cambridge, UK.

April 2019 to July 2022: Postdoctoral Fellow at AstraZeneca, Cambridge, UK

Education:

July 2019: Doctor of Philosophy, University of Liverpool, UK

June 2015: Master’s degree in Mathematics with Honors, Durham University, UK

Research

Mike's research interests focus on variational methods for image processing (in particular image segmentation and registration), machine learning for image and data analysis, image processing and data analysis. More recently, he has been focussing on best practice and scientific integrity in machine learning and data science, in particular for understanding the crisis of reproducibility affecting these fields. He has active interdisciplinary collaborations with other applied mathematicians, computer scientists and clinicians focussing on medical imaging problems. He has vast experience in studying high-dimensional data and medical imaging problems for lung diseases including (but not limited to) lung cancer, idiopathic lung fibrosis, mesothelioma and drug induced interstitial lung disease.

Publications

Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction
F Zhang, S Deltadahl, ML Delouee, D Kreuter, J Taylor, A Visser, B Consortium, JHF Rudd, NS Gleadall, S Sivapalaratnam, F Asselbergs, MC Schut, M Roberts
– 2026 2nd International Conference on Federated Learning and Intelligent Computing Systems Flics 2026
(2026)
00,
524
(doi: 10.1109/FLICS70075.2026.11621932)
PLAQUE STRUCTURAL STRESS ESTIMATIONS FROM ARTIFICIAL INTELLIGENCE-DRIVEN OCT ANALYSIS IMPROVE PREDICTION OF FUTURE MAJOR ADVERSE CARDIOVASCULAR EVENTS
J Sangha, Y Huang, S Gu, B Jessney, X Chen, CV Bourantas, L Raber, F Prati, M Roberts, M Bennett
– JACC-JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
(2026)
87,
A788
(link to publication)
26-A-15449-ACC PLAQUE STRUCTURAL STRESS ESTIMATIONS FROM ARTIFICIAL INTELLIGENCE-DRIVEN OCT ANALYSIS IMPROVE PREDICTION OF FUTURE MAJOR ADVERSE CARDIOVASCULAR EVENTS
J Sangha, Y Huang, S Gu, B Jessney, X Chen, CV Bourantas, L Raber, F Prati, M roberts, M Bennett
– Journal of the American College of Cardiology
(2026)
87,
a788
(doi: 10.1016/j.jacc.2026.02.2035)
Building Privacy-and-Security-Focused Federated Learning Infrastructure for Global Multi-Centre Healthcare Research
F Zhang, D Kreuter, J Fernandez-Marques, B Consortium, G Verghese, B Butler, N Lane, S Sivapalaratnam, J Taylor, NCJ de Wit, NS Gleadall, C-B Schönlieb, M Roberts
(2026)
(doi: 10.48550/arxiv.2603.10063)
Laplace Learning in Wasserstein Space
MCA Oliver, M Roberts, C-B Schönlieb, M Thorpe
(2025)
(link to publication)
FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems
F Zhang, D Kreuter, C Esteve-Yagüe, S Dittmer, J Fernandez-Marques, S Ip, B Consortium, NCJ de Wit, A Wood, JH Rudd, N Lane, NS Gleadall, C-B Schönlieb, M Roberts
(2025)
(doi: 10.48550/arxiv.2405.19000)
Enhanced visualisation of concealed target objects by infrared thermography and machine learning
W Khor, Y Chen, M Roberts, F Ciampa
– Infrared Physics and Technology
(2025)
152,
106186
(doi: 10.1016/j.infrared.2025.106186)
Artificial Intelligence-Led Whole Coronary Artery OCT Analysis; Validation and Identification of Drug Efficacy and Higher-Risk Plaques
B Jessney, X Chen, S Gu, Y Huang, M Goddard, A Brown, D Obaid, M Mahmoudi, HM Garcia Garcia, SP Hoole, L Räber, F Prati, C-B Schönlieb, M Roberts, M Bennett
– Circulation. Cardiovascular imaging
(2025)
18,
e018133
(doi: 10.1161/CIRCIMAGING.125.018133)
LEVERAGING ADULT BRAIN TUMOUR AI MODELS TO PREDICT PAEDIATRIC NEUROONCOLOGY SURVIVAL
T shadbahr, J Ruffle, L Burrows, I Palladino, J Gains, M Roberts, H Hyare
– Neuro-Oncology
(2025)
27,
ii15
(doi: 10.1093/neuonc/noaf185.057)
2-022 Artifact-corrected artificial intelligence-LED intracoronary oct analysis identifies plaque progression and vulnerability, drug efficacy and patient events
B Jessney, X Chen, S Gu, Y Huang, M Goddard, A Brown, D Obaid, M Mahmoudi, HM Garcia Garcia, SP Hoole, C Costopoulos, L Räber, F Prati, C-B Schönlieb, Y Onuma, P Serruys, M Roberts, M Bennett
– Acute coronary syndromes & interventional cardiology
(2025)
a60
(doi: 10.1136/heartjnl-2025-bcs.61)
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Research Group

Cambridge Image Analysis

Email

mr808@cam.ac.uk

Room

F1.13

Study at Cambridge

About the University

Research at Cambridge