用图注意力模型分析超声视频，精准定位病灶区域并提升可解释性。
GADA: Graph Attention-based Detection Aggregation for Ultrasound Video Classification
- 将视频分类转为时空图推理，用节点表示关键区域
- 在多中心肺部超声数据集上超越传统方法，准确率更高
- 输出可解释的区域与帧级注意力，适合临床辅助诊断
医学超声视频分析因序列长度不一、空间线索微弱且需可解释的视频级评估而具挑战性。本文提出基于图注意力的检测聚合框架GADA，将视频分类重构为对局部感兴趣区域的图推理问题。GADA在不同帧中检测与病理相关的区域，并将其作为时空图中的节点，边则编码空间与时间依赖关系。通过边感知的图注意力网络聚合节点预测，生成紧凑且具有判别力的视频级输出。在大规模多中心临床肺部超声数据集上，GADA在两项病理视频分类任务中均优于传统基线方法，同时提供可解释的区域级与帧级注意力。
原文摘要 · Abstract (English)
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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