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arXiv · 2510.11437

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

Li Chen·Naveen Balaraju·Jochen Kruecker·Balasundar Raju·Alvin Chen
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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.

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Li Chen, Naveen Balaraju, Jochen Kruecker, Balasundar Raju, Alvin Chen. 2025-10-13. GADA: Graph Attention-based Detection Aggregation for Ultrasound Video Classification. https://arxiv.org/abs/2510.11437

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