US-VLA: 腹部超音波検査のための視覚・言語・動作モデル
US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina
臨床的な意味目標を明示的に符号化し、リアルタイムの超音波フィードバックに基づいてプローブ操作を生成する超音波検査自動化のための視覚・言語・動作モデルを提案した。肝臓と腎臓の検査データセットを構築し、プローブ操作タスクでの有効性と汎化性を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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5. 議論はある？
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著者: Cheng Zhang, Xingzheng Wu, Guihao Yan, Xifeng Hu, Zhi Liu, Mei Wu, Qing Cai
分類: cs.RO, cs.CV
原文アブストラクト
Artificial intelligence-assisted ultrasound scanning enhances diagnostic reliability and efficiency by providing real-time guidance for standardized image acquisition and reducing operator dependence. However, existing reinforcement learning and learning-assisted ultrasound scanning methods typically rely on carefully designed reward functions or extensive interaction data, which limits their generalization ability and stability across different devices, patient populations, and complex clinical scenarios. To address these challenges, we propose an ultrasound vision-language-action model (US-VLA) for automated ultrasound scanning that explicitly encodes clinical semantic goals and generates sequential probe manipulation actions under real-time ultrasound feedback. In particular, we first design an ultrasound-aware expert fusion module to jointly integrate ultrasound observations with auxiliary contextual information, enabling semantic ultrasound feedback to effectively guide the scanning process. Then, we construct US-VLA-Data, a real-world dataset covering liver and kidney examinations, which includes five clinically defined standard planes and comprises 320 expert scanning trajectories with approximately 80,000 synchronized timesteps. Extensive experiments demonstrate that US-VLA achieves competitive performance in ultrasound probe manipulation tasks, indicating its effectiveness and promising generalization within the evaluated abdominal ultrasound setting. The source code is available at https://github.com/VMVLab/US-VLA.