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Publications

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Imaging the effects of tumour necrosis factor inhibition on pain processing in severe rheumatoid arthritis

Wartolowska K. et al, (2023)

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria

Bozek J. et al, (2023), NeuroImage, 119864 - 119864

Challenges for machine learning in clinical translation of big data imaging studies.

Dinsdale NK. et al, (2022), Neuron

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria

Bozek J. et al, (2022)

STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation

Dinsdale NK. et al, (2022), Medical Image Analysis, 102583 - 102583

FAST-IT: Find A Simple Test — In TIA (transient ischaemic attack): a prospective cohort study to develop a multivariable prediction model for diagnosis of TIA through proteomic discovery and candidate lipid mass spectrometry, neuroimaging and machine learning—study protocol

Milton AG. et al, (2022), BMJ Open, 12, e045908 - e045908

How certain are your uncertainties?

Whitbread L. and Jenkinson M., (2022)

Mutual information neural estimation for unsupervised multi-modal registration of brain images

Snaauw G. et al, (2022)

Uncertainty Categories in Medical Image Segmentation: A Study of Source-Related Diversity

Whitbread L. and Jenkinson M., (2022), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 26 - 35

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations

Wyburd MK. et al, (2021)

Challenges for machine learning in clinical translation of big data imaging studies

Dinsdale NK. et al, (2021)

Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging

To M-S. et al, (2021)

Brain tumour segmentation using a triplanar ensemble of U-Nets

Sundaresan V. et al, (2021)

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

Sundaresan V. et al, (2021)

White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance.

Melazzini L. et al, (2021), Neuroimage Clin, 30

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Boyd C. et al, (2021), DIAGNOSTICS, 11

Contrasting the brain imaging features of MOG-Antibody disease, with AQP4-Antibody NMOSD and Multiple Sclerosis

Messina S. et al, (2020)

Cortical diffusivity investigation in posterior cortical atrophy and typical Alzheimer's disease.

Torso M. et al, (2020), J Neurol

Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

Bordin V. et al, (2020)

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

Sundaresan V. et al, (2020)

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