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Faculty of Mathematics

 

Dr Michael Roberts

Mike Roberts is Principal Research Associate (Reader / Professor Grade 11) 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 also leads the algorithm development team for the global COVID-19 AIX-COVNET collaboration (https://covid19ai.maths.cam.ac.uk/).

Career

Positions:

October 2023 onwards: Principal Research Associate 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

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 et al.
– ArXiv
(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 et al.
– Nature machine intelligence
(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 Schonlieb, 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 et al.
– Nat Biomed Eng
(2021)
5,
943
(doi: 10.17863/CAM.74831)
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 et al.
– Nature biomedical engineering
(2021)
5,
509
(doi: 10.1038/s41551-021-00704-1)
Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise
D Driggs, I Selby, M Roberts, E Gkrania-Klotsas, J Rudd, G Yang, J Babar, E Sala, C Schoenlieb
– Radiology Artificial Intelligence
(2021)
3,
e210011
(doi: 10.17863/CAM.74057)
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, AI Aviles-Rivero, C Etmann, C McCague, L Beer, JR Weir-McCall, Z Teng, E Gkrania-Klotsas, A Ruggiero, A Korhonen, E Jefferson, E Ako, G Langs, G Gozaliasl, G Yang, H Prosch, J Preller, J Stanczuk, J Tang, J Hofmanninger et al.
– Nature Machine Intelligence
(2021)
3,
199
(doi: 10.1038/s42256-021-00307-0)
Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
EPV Le, L Rundo, JM Tarkin, NR Evans, MM Chowdhury, PA Coughlin, H Pavey, C Wall, F Zaccagna, FA Gallagher, Y Huang, R Sriranjan, A Le, JR Weir-McCall, M Roberts, FJ Gilbert, EA Warburton, C-B Schönlieb, E Sala, JHF Rudd
– Scientific reports
(2021)
11,
3499
(doi: 10.1038/s41598-021-82760-w)
Machine churning
M Roberts
– NEW SCIENTIST
(2021)
245,
23
(link to publication)
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, AI Aviles-Rivero, C Etmann, C McCague, L Beer, JR Weir-McCall, Z Teng, E Gkrania-Klotsas, JHF Rudd, E Sala, C-B Schönlieb
(2020)
(doi: 10.48550/arxiv.2008.06388)
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Research Group

Cambridge Image Analysis

Email

mr808@dpmms.cam.ac.uk

Room

F1.13

Telephone

01223 760390

Study at Cambridge

About the University

Research at Cambridge