21 citations · 85 across the 9 of their papers we have counts for
5 papers · 1 filter
Test-time Unsupervised Domain Adaptation
Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre +3
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain…
Hierarchical brain parcellation with uncertainty
Mark S. Graham, Carole H. Sudre, Thomas Varsavsky +4
Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods ten…
Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
David A. Wood, Jeremy Lynch, Sina Kafiabadi +13
Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a tr…
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…
Physics-informed brain MRI segmentation
Pedro Borges, Carole Sudre, Thomas Varsavsky +4
Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful medical imaging modalities. This flexibility does however come at a cost; MRI images acquired at different…