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Found 5 papers, 2 papers with code
Synthetic Data for Robust Stroke Segmentation
Deep learning-based semantic segmentation in neuroimaging currently requires high-resolution scans and extensive annotated datasets, posing significant barriers to clinical applicability.
Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI
The highest classification accuracy 0. 854 was observed when 8 regions-of-interest was extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network. Our findings demonstrate how imaging and tabular data can be combined for high post-stroke classification accuracy, even when the dataset is small in machine learning terms.
Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation
Vision transformers are effective deep learning models for vision tasks, including medical image segmentation.
Dynamic causal modelling of COVID-19
This technical report describes a dynamic causal model of the spread of coronavirus through a population.
Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI
One third of stroke survivors have language difficulties.
