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08/14/2023
Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation
Vision transformers are effective deep learning models for vision tasks,...
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05/27/2023
Deep Variational Lesion-Deficit Mapping
Causal mapping of the functional organisation of the human brain require...
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06/13/2022
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid....
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11/29/2021
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
We describe Countersynth, a conditional generative model of diffeomorphi...
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11/19/2021
Factorisation-based Image Labelling
Segmentation of brain magnetic resonance images (MRI) into anatomical re...
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08/24/2021
Correcting inter-scan motion artefacts in quantitative R1 mapping at 7T
Purpose: Inter-scan motion is a substantial source of error in R_1 estim...
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03/11/2021
A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets
Canonical Correlation Analysis (CCA) and its regularised versions have b...
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02/02/2021
Model-based multi-parameter mapping
Quantitative MR imaging is increasingly favoured for its richer informat...
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06/03/2020
Flexible Bayesian Modelling for Nonlinear Image Registration
We describe a diffeomorphic registration algorithm that allows groups of...
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05/28/2020
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific p...
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05/06/2020
Groupwise Multimodal Image Registration using Joint Total Variation
In medical imaging it is common practice to acquire a wide range of moda...
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09/03/2019
A Tool for Super-Resolving Multimodal Clinical MRI
We present a tool for resolution recovery in multimodal clinical magneti...
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08/16/2019
Empirical Bayesian Mixture Models for Medical Image Translation
Automatically generating one medical imaging modality from another is kn...
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07/26/2019
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Deep generative models are rapidly gaining traction in medical imaging. ...
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02/27/2019
Nonlinear Markov Random Fields Learned via Backpropagation
Although convolutional neural networks (CNNs) currently dominate competi...
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10/08/2018
MRI Super-Resolution using Multi-Channel Total Variation
This paper presents a generative model for super-resolution in routine c...
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07/27/2018
An Algorithm for Learning Shape and Appearance Models without Annotations
This paper presents a framework for automatically learning shape and app...
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07/18/2018
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
In this paper we present a method for simultaneously segmenting brain tu...
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06/19/2018
Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Shape modelling describes methods aimed at capturing the natural variabi...
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07/05/2017