GADA: Graph Attention-based Detection Aggregation for Ultrasound Video Classification
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914090272686080 |
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| author | Chen, Li Balaraju, Naveen Kruecker, Jochen Raju, Balasundar Chen, Alvin |
| author_facet | Chen, Li Balaraju, Naveen Kruecker, Jochen Raju, Balasundar Chen, Alvin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GADA: Graph Attention-based Detection Aggregation for Ultrasound Video Classification Chen, Li Balaraju, Naveen Kruecker, Jochen Raju, Balasundar Chen, Alvin Image and Video Processing 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. |
| title | GADA: Graph Attention-based Detection Aggregation for Ultrasound Video Classification |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2510.11437 |