26 citations · 87 across the 21 of their papers we have counts for
4 papers · 1 filter
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…
Nonlinear Markov Random Fields Learned via Backpropagation
Mikael Brudfors, Yaël Balbastre, John Ashburner
Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on…