12 citations · 14 across the 3 of their papers we have counts for
9 papers
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…
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…
Towards Quantifying Neurovascular Resilience
Stefano Moriconi, Rafael Rehwald, Maria A. Zuluaga +4
Whilst grading neurovascular abnormalities is critical for prompt surgical repair, no statistical markers are currently available for predicting the risk of adverse events, such as…
Unsupervised Videographic Analysis of Rodent Behaviour
Anthony Bourached, Parashkev Nachev
Animal behaviour is complex and the amount of data in the form of video, if extracted, is copious. Manual analysis of behaviour is massively limited by two insurmountable obstacles…
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre +9
Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this cha…