467 citations
- University College LondonGB54 papers
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- King's College LondonGB9 papers
- National Hospital for Neurology and NeurosurgeryGB8 papers
- UCL Queen Square Institute of NeurologyGB8 papers
- Max Planck Institute of Animal BehaviorDE7 papers
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64 papers
Reconstructing MRI Parameters Using a Noncentral Chi Noise Model
Klara Baś, Christian Lambert, John Ashburner
Quantitative magnetic resonance imaging (qMRI) allows images to be compared across sites and time points, which is particularly important for assessing long-term conditions or for…
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
Synthetic Data for Robust Stroke Segmentation
Liam Chalcroft, Ioannis Pappas, Cathy J. Price +1
Current deep learning-based approaches to lesion segmentation in neuroimaging often depend on high-resolution images and extensive annotated data, limiting clinical applicability.…
On Predictive planning and counterfactual learning in active inference
Aswin Paul, Takuya Isomura, Adeel Razi
Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general th…
Automatic Segmentation of the Spinal Cord Nerve Rootlets
Jan Valosek, Theo Mathieu, Raphaelle Schlienger +2
Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develo…
Bayesian sparsification for deep neural networks with Bayesian model reduction
Dimitrije Marković, Karl J. Friston, Stefan J. Kiebel
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsi…