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Mar 3, 2023 · Graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction.
Multi-modal integration and classification based on graph learning is among the most challenging obstacles in disease prediction due to its complexity.
An end-to-end Multi-modal Graph Learning framework (MMGL) for disease prediction with multi-modality is proposed to aggregate the features of each modality.
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery ... To effectively offset the negative impact between modalities in the ...
This study explores a novel approach to enhance the accuracy and reliability of ASD diagnosis by integrating resting-state functional magnetic resonance ...
Missing: Discovery. | Show results with:Discovery.
Multi-modal integration and classification based on graph learning is among the most challenging obstacles in disease prediction due to its complexity. Several ...
May 2, 2024 · In this study, we propose a deep learning framework for autism prediction using multimodal feature fusion and hypergraph neural networks (HGNN) ...
Missing: kernel | Show results with:kernel
Jun 20, 2024 · Self-attention is suitable to the graphs of various shapes and sizes and dynamic node interactions, thereby captures better information within ...
Missing: Discovery. | Show results with:Discovery.
A novel multi-site graph learning method named DMSGCN is introduced and shows a promising ASD identification performance.
Multi-modal integration and classification based on graph learning is among the most challenging obstacles in disease prediction due to its ...