Vision transformers are effective deep learning models for vision tasks,...
Data used in image segmentation are not always defined on the same grid....
To date few studies have comprehensively compared medical image registra...
Segmentation of brain magnetic resonance images (MRI) into anatomical re...
Purpose: Inter-scan motion is a substantial source of error in R_1
estim...
Quantitative MR imaging is increasingly favoured for its richer informat...
We describe a diffeomorphic registration algorithm that allows groups of...
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific
p...
In medical imaging it is common practice to acquire a wide range of
moda...
We present a tool for resolution recovery in multimodal clinical magneti...
Automatically generating one medical imaging modality from another is kn...
We predicted residual fluid intelligence scores from T1-weighted MRI dat...
We applied several regression and deep learning methods to predict fluid...
Although convolutional neural networks (CNNs) currently dominate competi...
This paper presents a generative model for super-resolution in routine
c...
This paper presents a framework for automatically learning shape and
app...
Shape modelling describes methods aimed at capturing the natural variabi...