Proposes the I2B-HGNN framework using information bottleneck-guided graph transformers and heterogeneous graph attention for interpretable multimodal NDD diagnosis.
GATE: Graph CCA for temporal self-supervised learning for label-efficient fMRI analysis
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Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
Proposes the I2B-HGNN framework using information bottleneck-guided graph transformers and heterogeneous graph attention for interpretable multimodal NDD diagnosis.