GADA: Graph Attention-based Detection Aggregation for Ultrasound Video Classification

2025/10/13 by Chen, Li, Balaraju, Naveen, Kruecker, Jochen +2
#FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2510.11437

Abstract

Medical ultrasound video analysis is challenging due to variable sequence lengths, subtle spatial cues, and the need for interpretable video-level assessment. We introduce GADA, a Graph Attention-based Detection Aggregation framework that reformulates video classification as a graph reasoning problem over spatially localized regions of interest. Rather than relying on 3D CNNs or full-frame analysis, GADA detects pathology-relevant regions across frames and represents them as nodes in a spatiotemporal graph, with edges encoding spatial and temporal dependencies. A graph attention network aggregates these node-level predictions through edge-aware attention to generate a compact, discriminative video-level output. Evaluated on a large-scale, multi-center clinical lung ultrasound dataset, GADA outperforms conventional baselines on two pathology video classification tasks while providing interpretable region- and frame-level attention.

Citations

• Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound Videos
• TASL-Net: Tri-Attention Selective Learning Network for Intelligent Diagnosis of Bimodal Ultrasound Video
• Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers
• EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
• Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos
• Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
• Adam: A Method for Stochastic Optimization
• Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
• Long Short-Term Memory
• Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages

Related

• Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
• TASL-Net: Tri-Attention Selective Learning Network for Intelligent Diagnosis of Bimodal Ultrasound Video
• UESA-Net: U-Shaped Embedded Multidirectional Shrinkage Attention Network for Ultrasound Nodule Segmentation
• Attention-Gated Networks for Improving Ultrasound Scan Plane Detection
• Weakly Semi-Supervised Detection in Lung Ultrasound Videos