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Found 9 papers, 3 papers with code
Generalizable automated ischaemic stroke lesion segmentation with vision transformers
Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury.
Deep generative computed perfusion-deficit mapping of ischaemic stroke
Analysing computed perfusion maps from 1, 393 CTA-imaged-patients with acute ischaemic stroke, we use deep generative inference to localise neural substrates of NIHSS sub-scores.
Computational limits to the legibility of the imaged human brain
It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.
The minimal computational substrate of fluid intelligence
The quantification of cognitive powers rests on identifying a behavioural task that depends on them.
Deep Variational Lesion-Deficit Mapping
Here we initiate the application of deep generative neural network architectures to the task of lesion-deficit inference, formulating it as the estimation of an expressive hierarchical model of the joint lesion and deficit distributions conditioned on a latent neural substrate.
Individualized prescriptive inference in ischaemic stroke
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, based on simple descriptions of presumptively homogeneous populations.
Brain tumour genetic network signatures of survival
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology.
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low.
