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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

Reinterpreting survival analysis in the universal approximator age
S Dittmer, M Roberts, J Preller, AIX COVNET, JHF Rudd, JAD Aston, C-B Schönlieb
(2023)
Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data
D Kreuter, S Tull, J Gilbey, J Preller, B Consortium, JAD Aston, JHF Rudd, S Sivapalaratnam, C-B Schönlieb, N Gleadall, M Roberts
(2023)
A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data.
A Breger, I Selby, M Roberts, J Babar, E Gkrania-Klotsas, J Preller, L Escudero Sanchez, J Rudd, J Aston, J Weir-McCall, E Sala, C Schoenlieb
– Sci Data
(2023)
10,
493
Navigating the development challenges in creating complex data systems
S Dittmer, M Roberts, J Gilbey, A Biguri, I Selby, A Breger, M Thorpe, JR Weir-McCall, E Gkrania-Klotsas, A Korhonen, E Jefferson, G Langs, G Yang, H Prosch, J Stanczuk, J Tang, J Babar, L Escudero Sánchez, P Teare, M Patel, M Wassin, M Holzer, N Walton, P Lió, T Shadbahr, E Sala, J Preller, JHF Rudd, JAD Aston, CB Schönlieb
– Nature Machine Intelligence
(2023)
5,
681
GLPG1205 shows reduction in lung volume decline over 26 weeks vs placebo when measured with novel volumetric CT analysis in IPF patients
M Thillai, K Kirov, E Santermans, M Roberts, P Molyneaux, F Kanavati, D Gallagher, A De Haas-Amatsaleh, T Van Der Aa, P Ford, C Seemayer, B Van Den Blink, A Ruggiero
– 12.01 - Idiopathic interstitial pneumonias
(2022)
836
Data-driven multivariate cohort selection and automated CT scan lung volume measurement identifies disease progression in Idiopathic Pulmonary Fibrosis (IPF)
H Manners, T Mclellan, K Kirov, M Roberts, F Kanavati, G Mckenzie, D Gallagher, J Hainsworth, D Dosanjh, P Molyneaux, A Ruggiero, M Thillai
– 12.01 - Idiopathic interstitial pneumonias
(2022)
4502
USING ARTIFICIAL INTELLIGENCE TO INTERROGATE MULTI-NATIONAL IMAGING DATASETS TO DETERMINE THE MECHANISM OF COVID-19 PNEUMOTHORAX
IA Selby, D Driggs, V Majcher, M Roberts, LE Sanchez, JHF Rudd, E Sala, C Bibiane-Schonlieb, SJ Marciniak, J Babar
– ‘Infinity War’ – Ongoing clinical challenges in COVID-19
(2022)
77,
a169.3
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)
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)
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
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Research Group

Cambridge Image Analysis

Room

F1.13

Telephone

01223 760390