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Cambridge Mathematics of Information in Healthcare


CMIH publications are held in a central repository that can be accessed by clicking the button below. We fully support the concept of universal access so that the wider research community can view and use the research published from within the Centre.


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Key Publications:


  • Sabaté Landman et al, (2023), On Krylov methods for large-scale CBCT reconstruction. (DOI: 10.1088/1361-6560/acd616)

  • Wei et al, (2023), Multi-modal learning for predicting the genotype of glioma. (DOI: 10.1109/tmi.2023.3244038)

  • Aviles-Rivero et al, (2022), GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays. (DOI: 10.1016/j.patcog.2021.108274)

  • Huang et al, (2022), Estimation of the zero-pressure computational start shape of atherosclerotic plaques: Improving the backward displacement method with deformation gradient tensor. (DOI: 10.1016/j.jbiomech.2021.110910)

  • Stickels et al, (2022), Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality. (DOI: 10.1136/bmjopen-2021-059309)

  • Aviles-Rivero et al, (2021), Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction (DOI: 10.1016/

  • Le et al, (2021) Assessing Robustness of Carotid Artery CT Angiography Radiomics in the Identification of Culprit lesions in Cerebrovascular Events (DOI: 10.1038/s41598-021-82760-w)

  • Li et al, (2021), BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementia Classification (DOI: 10.1007/978-3-030-88210-5_9)

  • Lui et al, (2021), Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction (DOI: 10.1016/

  • Roberts et al, (2021), Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans (DOI: 10.1038/s42256-021-00307-0)

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