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Found 11 papers, 4 papers with code
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.
Individualized prescriptive inference in ischaemic stroke
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials.
Representational Ethical Model Calibration
Equity is widely held to be fundamental to the ethics of healthcare.
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid.
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling.
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.
Neuradicon: operational representation learning of neuroimaging reports
Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis.
An MRF-UNet Product of Experts for Image Segmentation
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-distribution data.
Unsupervised Brain Anomaly Detection and Segmentation with Transformers
Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic.
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts.
Construct validation of a DCM for resting state fMRI
We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences.
