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

Li Chen Naveen Balaraju Jochen Kruecker Balasundar Raju Alvin Chen Philips

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.

Published 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

https://ieeexplore.ieee.org/document/11375471/

PDF: https://arxiv.org/pdf/2510.11437