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US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina — arXiv:2608.16074

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16074
License (as archived): CC0-1.0
Archived: 2026-10-18T23:05:59.396Z
SHA-256 of the archived text: 5bfe98643e326cd99a55b4c0808e2bac56313026d04ac9139c38c4f1c2fe3223
Stable URL of this page: https://metraipolis.com/documento/dc_116a24f0c729

How to cite

US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina — arXiv:2608.16074 — arXiv (en), https://arxiv.org/abs/2608.16074 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_116a24f0c729 (Metraipolis archive dc_116a24f0c729, sha256 5bfe98643e326cd99a55b4c0808e2bac56313026d04ac9139c38c4f1c2fe3223)

BibTeX

@misc{dc116a24f0c729,
  title = {US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina — arXiv:2608.16074},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16074},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_116a24f0c729; sha256 5bfe98643e326cd99a55b4c0808e2bac56313026d04ac9139c38c4f1c2fe3223},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16074. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-18 in cs.RO).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.16074 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T23:01:50.374Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).