Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Chen, Li, Balaraju, Naveen, Kruecker, Jochen, Raju, Balasundar, Chen, Alvin
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
Award: G2487038225
OpenAlex ID: W7091187379
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