Public articles linked to the same research event.
arXiv The work presents ZAGNet, a zone-aware graph neural network that represents temporally tracked lung ultrasound pathology findings as graph nodes connected by anatomical zone adjacency, uses a graph transformer to propagate context across neighboring lung regions, and aggregates graph-level features through a virtual global node to predict patient-level consolidation and pleural effusion under patient-level supervision only; on a multicenter dataset of 714 subjects and 20,256 LUS video loops with exams spanning 4 to 16 zones across anterior, posterior, and lateral thoracic regions, AUC was 0.803 for consolidation (max pooling 0.677, mean pooling 0.674) and 0.893 for pleural effusion (max pooling 0.804, mean pooling 0.815), with missing zones accommodated by computing on a graph structure.
The work presents ZAGNet, a zone-aware graph neural network that represents temporally tracked lung ultrasound pathology findings as graph nodes connected by anatomical zone adjacency, uses a graph transformer to propagate context across neighboring lung regions, and aggregates graph-level features through a virtual global node to predict patient-level consolidation and pleural effusion under patient-level supervision only; on a multicenter dataset of 714 subjects and 20,256 LUS video loops with exams spanning 4 to 16 zones across anterior, posterior, and lateral thoracic regions, AUC was 0.803 for consolidation (max pooling 0.677, mean pooling 0.674) and 0.893 for pleural effusion (max pooling 0.804, mean pooling 0.815), with missing zones accommodated by computing on a graph structure.
The work presents ZAGNet, a zone-aware graph neural network that represents temporally tracked lung ultrasound pathology findings as graph nodes connected by anatomical zone adjacency, uses a graph transformer to propagate context across neighboring lung regions, and aggregates graph-level features through a virtual global node to predict patient-level consolidation and pleural effusion under patient-level supervision only; on a multicenter dataset of 714 subjects and 20,256 LUS video loops with exams spanning 4 to 16 zones across anterior, posterior, and lateral thoracic regions, AUC was 0.803 for consolidation (max pooling 0.677, mean pooling 0.674) and 0.893 for pleural effusion (max pooling 0.804, mean pooling 0.815), with missing zones accommodated by computing on a graph structure.
The work presents ZAGNet, a zone-aware graph neural network that represents temporally tracked lung ultrasound pathology findings as graph nodes connected by anatomical zone adjacency, uses a graph transformer to propagate context across neighboring lung regions, and aggregates graph-level features through a virtual global node to predict patient-level consolidation and pleural effusion under patient-level supervision only; on a multicenter dataset of 714 subjects and 20,256 LUS video loops with exams spanning 4 to 16 zones across anterior, posterior, and lateral thoracic regions, AUC was 0.803 for consolidation (max pooling 0.677, mean pooling 0.674) and 0.893 for pleural effusion (max pooling 0.804, mean pooling 0.815), with missing zones accommodated by computing on a graph structure.