26 citations · 98 across the 19 of their papers we have counts for
9 papers · 1 filter
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Factorisation-based Image Labelling
Yu Yan, Yael Balbastre, Mikael Brudfors +1
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully…
An MRF-UNet Product of Experts for Image Segmentation
Mikael Brudfors, Yaël Balbastre, John Ashburner +4
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribu…
Model-based multi-parameter mapping
Yael Balbastre, Mikael Brudfors, Michela Azzarito +3
Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encodin…
Flexible Bayesian Modelling for Nonlinear Image Registration
Mikael Brudfors, Yaël Balbastre, Guillaume Flandin +2
We describe a diffeomorphic registration algorithm that allows groups of images to be accurately aligned to a common space, which we intend to incorporate into the SPM software. Th…
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…
MRI Super-Resolution using Multi-Channel Total Variation
Mikael Brudfors, Yael Balbastre, Parashkev Nachev +1
This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recove…
An Algorithm for Learning Shape and Appearance Models without Annotations
John Ashburner, Mikael Brudfors, Kevin Bronik +1
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. It is based on the idea that having a more accurate s…
Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Yaël Balbastre, Mikael Brudfors, Kevin Bronik +1
Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques s…