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In this work, we combine CNNs with graph neural networks (GNNs) to jointly learn an adjacency matrix of connectivity's between ROIs as a prior for learning ...
Combining graph neural networks and ROI-based convolutional neural networks to infer individualized graphs for Alzheimer's prediction. K Mueller, A Meyer-Baese, ...
Feb 1, 2024 · In this study, we focused on multimodal data to predict AD and enhance the explainability and interpretability of prediction models. To process ...
Missing: individualized | Show results with:individualized
Combining these features leads to improved performance than using a single modality alone in building predictive models for AD diagnosis. However, current multi ...
Missing: individualized | Show results with:individualized
Combining graph neural networks and ROI-based convolutional neural networks to infer individualized graphs for Alzheimer's prediction.
Jan 27, 2023 · Diagnosis of Alzheimer's disease severity with fMRI images using robust multitask feature extraction method and convolutional neural network (CNN)
First application of graph convolutional networks for brain analysis in populations. · Graph based population model that leverages imaging and non-imaging data.
Missing: individualized | Show results with:individualized
This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning.
Oct 16, 2023 · A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects.
Missing: infer | Show results with:infer
This study aimed to employ a spectral graph convolutional neural network (graph-CNN), that incorporated cortical thickness and geometry, to identify MCI and AD.
Missing: individualized | Show results with:individualized