The quantification of cognitive powers rests on identifying a behavioura...
Recent advances in generative AI have brought incredible breakthroughs i...
Methods for out-of-distribution (OOD) detection that scale to 3D data ar...
We propose a new method, Patch-CNN, for diffusion tensor (DT) estimation...
Causal mapping of the functional organisation of the human brain require...
Out-of-distribution detection is crucial to the safe deployment of machi...
Deep neural networks have brought remarkable breakthroughs in medical im...
Human anatomy, morphology, and associated diseases can be studied using
...
Equity is widely held to be fundamental to the ethics of healthcare. In ...
This paper demonstrates spherical convolutional neural networks (S-CNN) ...
Data used in image segmentation are not always defined on the same grid....
Background: The complex heterogeneity of brain tumours is increasingly
r...
This work introduces a scaffolding framework to compactly parametrise so...
Deep generative models have emerged as promising tools for detecting
arb...
In a clinical setting it is essential that deployed image processing sys...
The characteristics and determinants of health and disease are often
org...
We describe Countersynth, a conditional generative model of diffeomorphi...
Models of human motion commonly focus either on trajectory prediction or...
The value of biomedical research–a 1.7 trillion annual investment–is
ult...
The use of electronic health records in medical research is difficult be...
Convolutional neural networks trained on publicly available medical imag...
We describe a diffeomorphic registration algorithm that allows groups of...
The increasing efficiency and compactness of deep learning architectures...
Whilst grading neurovascular abnormalities is critical for prompt surgic...
Animal behaviour is complex and the amount of data in the form of video,...
We present a tool for resolution recovery in multimodal clinical magneti...
Supervised learning algorithms trained on medical images will often fail...
Automatically generating one medical imaging modality from another is kn...
Deep generative models are rapidly gaining traction in medical imaging.
...
This paper presents a generative model for super-resolution in routine
c...
Vascular graphs can embed a number of high-level features, from morpholo...
In a research context, image acquisition will often involve a pre-define...
The analysis of vessel morphology and connectivity has an impact on a nu...
Medical image analysis and computer-assisted intervention problems are
i...