A self-clustering graph transformer that tailors attention per brain subnetwork slightly improves cognitive score and gender prediction from resting-state fMRI and yields interpretable network assignments.
Effective training strategy for NN models of working memory classification with limited samples,
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Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity
A self-clustering graph transformer that tailors attention per brain subnetwork slightly improves cognitive score and gender prediction from resting-state fMRI and yields interpretable network assignments.