12 citations · 35 across the 9 of their papers we have counts for
6 papers · 1 filter
Acquisition-invariant brain MRI segmentation with informative uncertainties
Pedro Borges, Richard Shaw, Thomas Varsavsky +5
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any down…
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
Pedro Borges, Richard Shaw, Thomas Varsavsky +5
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter depen…
A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +2
Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visua…
Estimating MRI Image Quality via Image Reconstruction Uncertainty
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +1
Quality control (QC) in medical image analysis is time-consuming and laborious, leading to increased interest in automated methods. However, what is deemed suitable quality for alg…
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw +5
The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical vo…
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +1
Quality control (QC) of medical images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed…