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

Navigating the challenges in creating complex data systems: a development philosophy

S Dittmer, M Roberts, J Gilbey, A Biguri, AIX-COVNET Collaboration, J Preller, JHF Rudd, JAD Aston, C-B Schönlieb

(2022)

(doi: 10.48550/arxiv.2210.13191)

Classification of datasets with imputed missing values: does imputation quality matter?

T Shadbahr, M Roberts, J Stanczuk, J Gilbey, P Teare, S Dittmer, M Thorpe, RV Torne, E Sala, P Lio, M Patel, AIX-COVNET Collaboration, JHF Rudd, T Mirtti, A Rannikko, JAD Aston, J Tang, C-B Schönlieb

(2022)

(doi: 10.48550/arxiv.2206.08478)

146 Ct radiomics in carotid artery atherosclerosis: a systematic evaluation of robustness, reproducibility and predictive performance for culprit lesions

E Le, L Rundo, J Tarkin, N Evans, M Chowdhury, P Coughlin, H Pavey, C Wall, F Zaccagna, F Gallagher, Y Huang, R Sriranjan, A Le, J Weir-McCall, M Roberts, F Gilbert, E Warburton, C-B Schonlieb, E Sala, J Rudd

– Imaging

(2022)

108,

A112.1

(doi: 10.1136/heartjnl-2022-bcs.146)

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Y Nan, JD Ser, S Walsh, C Schönlieb, M Roberts, I Selby, K Howard, J Owen, J Neville, J Guiot, B Ernst, A Pastor, A Alberich-Bayarri, MI Menzel, S Walsh, W Vos, N Flerin, J-P Charbonnier, E van Rikxoort, A Chatterjee, H Woodruff, P Lambin, L Cerdá-Alberich, L Martí-Bonmatí, F Herrera, G Yang

– Inf Fusion

(2022)

82,

(doi: 10.1016/j.inffus.2022.01.001)

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,

(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,

(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,

Medical Image Understanding and Analysis

(2022)

(doi: 10.1007/978-3-031-12053-4)

AI and Point of Care Image Analysis for COVID-19

M Roberts, O Frank, S Bagon, YC Eldar, CB Schönlieb

(2022)

(doi: 10.1007/978-3-031-08506-2_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, LE Sanchez, E Sala, DL Rubin, A Weller, J Lasenby, C Zheng, J Wang, Z Li, C Schönlieb, T Xia

– Nat Mach Intell

(2021)

1081

(doi: 10.1038/s42256-021-00421-z)

Cambridge Image Analysis

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