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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 ...
Apr 10, 2023 · 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, ...
Combining graph neural networks and ROI-based convolutional neural networks to infer individualized graphs for Alzheimer's prediction.
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
In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of ...
Jan 27, 2023 · Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological ...
First application of graph convolutional networks for brain analysis in populations. · Graph based population model that leverages imaging and non-imaging data.
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This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning.
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 ...