41 citations · 261 across the 45 of their papers we have counts for
6 papers · 1 filter
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…
A Tool for Super-Resolving Multimodal Clinical MRI
Mikael Brudfors, Yael Balbastre, Parashkev Nachev +1
We present a tool for resolution recovery in multimodal clinical magnetic resonance imaging (MRI). Such images exhibit great variability, both biological and instrumental. This var…
Empirical Bayesian Mixture Models for Medical Image Translation
Mikael Brudfors, John Ashburner, Parashkev Nachev +1
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpre…
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Guilherme Pombo, Robert Gray, Tom Varsavsky +2
Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of v…