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Found 5 papers, 0 papers with code
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification
Using quantitative R2* maps we separated Alzheimer's patients (n=117) from normal controls (n=219) by using a convolutional neural network and systematically investigated the learned concepts using Concept Relevance Propagation and compared these results to a conventional region of interest-based analysis.
Imputation of missing values in multi-view data
This leads to very large quantities of missing data which, especially when combined with high-dimensionality, makes the application of conditional imputation methods computationally infeasible.
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure.
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity
As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing.
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations.
