Public articles linked to the same research event.
arXiv The work proposes BrainSTR, a spatio-temporal contrastive learning framework that learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity; a spatio-temporal supervised contrastive learning approach then leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and build a well-structured semantic space. Experiments on ASD, BD, and MDD validate its effectiveness, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings.
The work proposes BrainSTR, a spatio-temporal contrastive learning framework that learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity; a spatio-temporal supervised contrastive learning approach then leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and build a well-structured semantic space. Experiments on ASD, BD, and MDD validate its effectiveness, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings.
The work proposes BrainSTR, a spatio-temporal contrastive learning framework that learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity; a spatio-temporal supervised contrastive learning approach then leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and build a well-structured semantic space. Experiments on ASD, BD, and MDD validate its effectiveness, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings.
The work proposes BrainSTR, a spatio-temporal contrastive learning framework that learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity; a spatio-temporal supervised contrastive learning approach then leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and build a well-structured semantic space. Experiments on ASD, BD, and MDD validate its effectiveness, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings.