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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

Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review.
N Sushentsev, N Moreira Da Silva, M Yeung, T Barrett, E Sala, M Roberts, L Rundo
– Insights into Imaging
(2022)
13,
59
(doi: 10.1186/s13244-022-01199-3)
Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review
N Sushentsev, N Moreira Da Silva, M Yeung, T Barrett, E Sala, M Roberts, L Rundo
– Insights into Imaging
(2022)
13,
59
(doi: 10.1186/s13244-022-01199-3)
Preface
G Yang, A Aviles-Rivero, M Roberts, CB Schönlieb
– Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
(2022)
13413 LNCS,
v
AI and Point of Care Image Analysis for COVID-19
M Roberts, O Frank, S Bagon, YC Eldar, C-B Schönlieb
(2022)
85
(doi: 10.1007/978-3-031-08506-2_4)
Medical Image Understanding and Analysis
(2022)
(doi: 10.1007/978-3-031-12053-4)
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.
X Bai, H Wang, L Ma, Y Xu, J Gan, Z Fan, F Yang, K Ma, J Yang, S Bai, C Shu, X Zou, R Huang, C Zhang, X Liu, D Tu, C Xu, W Zhang, X Wang, A Chen, Y Zeng, D Yang, M-W Wang, N Holalkere, NJ Halin, IR Kamel, J Wu, X Peng, X Wang, J Shao, P Mongkolwat, J Zhang, W Liu, M Roberts, Z Teng, L Beer, L Escudero Sanchez, E Sala, D Rubin, A Weller, J Lasenby, C Zheng, J Wang, Z Li, C-B Schönlieb, T Xia
– Nature Machine Intelligence
(2021)
3,
1081
(doi: 10.1038/s42256-021-00421-z)
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.
X Bai, H Wang, L Ma, Y Xu, J Gan, Z Fan, F Yang, K Ma, J Yang, S Bai, C Shu, X Zou, R Huang, C Zhang, X Liu, D Tu, C Xu, W Zhang, X Wang, A Chen, Y Zeng, D Yang, M-W Wang, N Holalkere, NJ Halin, IR Kamel, J Wu, X Peng, X Wang, J Shao, P Mongkolwat, J Zhang, W Liu, M Roberts, Z Teng, L Beer, LE Sanchez, E Sala, DL Rubin, A Weller, J Lasenby, C Zheng, J Wang, Z Li, C Schönlieb, T Xia
– Nat Mach Intell
(2021)
3,
1081
(doi: 10.1038/s42256-021-00421-z)
Late Breaking Abstract - Fully automated airway measurement correlates with radiological disease progression in Idiopathic Pulmonary Fibrosis
M Roberts, K Kirov, T Mclellan, E Morgan, F Kanavati, D Gallagher, P Molyneaux, C-B Schönlieb, A Ruggiero, M Thillai
– m-Health/e-health
(2021)
58,
oa3951
(doi: 10.1183/13993003.congress-2021.oa3951)
Author Correction: A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images (Nature Biomedical Engineering, (2021), 5, 6, (509-521), 10.1038/s41551-021-00704-1)
G Wang, X Liu, J Shen, C Wang, Z Li, L Ye, X Wu, T Chen, K Wang, X Zhang, Z Zhou, J Yang, Y Sang, R Deng, W Liang, T Yu, M Gao, J Wang, Z Yang, H Cai, G Lu, L Zhang, L Yang, W Xu, W Wang, A Olvera, I Ziyar, C Zhang, O Li, W Liao, J Liu, W Chen, W Chen, J Shi, L Zheng, L Zhang, Z Yan, X Zou, G Lin, G Cao, LL Lau, L Mo, Y Liang, M Roberts, E Sala, C-B Schönlieb, M Fok, JY-N Lau, T Xu, J He, K Zhang, W Li, T Lin
– Nature biomedical engineering
(2021)
5,
943
(doi: 10.1038/s41551-021-00787-w)
A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images
G Wang, X Liu, J Shen, C Wang, Z Li, L Ye, X Wu, T Chen, K Wang, X Zhang, Z Zhou, J Yang, Y Sang, R Deng, W Liang, T Yu, M Gao, J Wang, Z Yang, H Cai, G Lu, L Zhang, L Yang, W Xu, W Wang, A Olvera, I Ziyar, C Zhang, O Li, W Liao, J Liu, W Chen, W Chen, J Shi, L Zheng, L Zhang, Z Yan, X Zou, G Lin, G Cao, LL Lau, L Mo, Y Liang, M Roberts, E Sala, C-B Schönlieb, M Fok, JY-N Lau, T Xu, J He, K Zhang, W Li, T Lin
– Nat Biomed Eng
(2021)
5,
509
(doi: 10.1038/s41551-021-00704-1)
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Research Group

Cambridge Image Analysis

Email

mr808@cam.ac.uk

Room

F1.13

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About the University

Research at Cambridge