Michigan Technological University

Structural MRI brain graphs no longer rely on a single predefined connection rule. MVMGNN treats brain regions as nodes carrying radiomics features, builds a spatial-neighbor graph and a feature-similarity graph, filters redundant features and weak connections inside each view with a joint node-edge mask, and fuses the two views through patient-level gated fusion. On the ADNI cohort, AD versus CN accuracy rises from 82.06% for the best comparison method, RSEA-MVGNN, to 89.90%, and F1 from 75.02% to 86.24%; gains on the harder AD-MCI and CN-MCI tasks are smaller.

Structural MRI brain graphs no longer rely on a single predefined connection rule. MVMGNN treats brain regions as nodes carrying radiomics features, builds a spatial-neighbor graph and a feature-similarity graph, filters redundant features and weak connections inside each view with a joint node-edge mask, and fuses the two views through patient-level gated fusion. On the ADNI cohort, AD versus CN accuracy rises from 82.06% for the best comparison method, RSEA-MVGNN, to 89.90%, and F1 from 75.02% to 86.24%; gains on the harder AD-MCI and CN-MCI tasks are smaller.