FENet combines time- and frequency-domain brain network views in a CCA-based self-supervised framework, improving psychiatric disorder classification accuracy on ABIDE and ADHD-200 over prior graph SSL methods.
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Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks
FENet combines time- and frequency-domain brain network views in a CCA-based self-supervised framework, improving psychiatric disorder classification accuracy on ABIDE and ADHD-200 over prior graph SSL methods.