Janet L Welch, Katie A Siek, Kay H Connelly, Kim S Astroth, M Sue McManus, Linda Scott, Seongkum Heo, Michael A Kraus
Merging health literacy with computer technology: self-managing diet and fluid intake among adult hemodialysis patients
Patient education and counseling
many fully automatic segmentation models created solve difficulty brain tumor segmentation thanks rapid growth deep learning however approaches focus longrange relationships contextual interdependence multimodal magnetic resonance mr images paper propose novel approach brain tumor segmentation called dual graph reasoning unit dgrunit two parallel graph reasoning modules included proposed method spatial reasoning module channel reasoning module spatial reasoning module models longrange spatial dependencies distinct regions image using graph convolutional network gcn channel reasoning module uses graph attention network gat model rich contextual interdependencies different channels similar semantic representations experimental results clearly demonstrate superior performance proposed dgrunit ablation study shows flexibility generalizability model easily integrated wide range neural networks improve compared several stateoftheart methods experimental results show proposed approach significantly improves visual inspection quantitative metrics brain tumor segmentation tasks