Ch K Kydona, G Malamis, T Giasnetsova, V Tsiora, N Gritsi-Gerogianni
The level of teamwork as an index of quality in ICU performance
Hippokratia
recently study multimodal brain connectome recorded tremendous increase facilitated diagnosis brain disorders paradigm functional structural networks eg functional structural connectivity derived fmri dti manner interacted necessarily linearly related accordingly remains great challenge leverage complementary information brain connectome analysis recently graph convolutional networks gnn widely applied fusion multimodal brain connectome however existing gnn methods fail couple intermodal relationships regard propose crossmodal graph neural network crossgnn captures intermodal dependencies dynamic graph learning mutual learning specifically intermodal representations attentively coupled compositional space reasoning intermodal dependencies additionally investigate mutual learning explicit implicit ways 1 crossmodal representations obtained crossembedding explicitly based intermodal correspondence matrix 2 propose crossmodal distillation method implicitly regularize latent representations crossmodal semantic contexts carry statistical analysis attentively learned correspondence matrices evaluate intermodal relationships associating disease biomarkers extensive experiments three datasets demonstrate superiority proposed method disease diagnosis promising prediction performance multimodal connectome biomarker location