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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.06347 |
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| _version_ | 1866911574387589120 |
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| author | Huang, Peng Wang, Yiming Chen, Yineng Gui, Liangqiao Guo, Hui Peng, Bo Hu, Shu Wu, Xi Connie, Tsao Zhu, Hongtu Prabhakaran, Balakrishnan Wang, Xin |
| author_facet | Huang, Peng Wang, Yiming Chen, Yineng Gui, Liangqiao Guo, Hui Peng, Bo Hu, Shu Wu, Xi Connie, Tsao Zhu, Hongtu Prabhakaran, Balakrishnan Wang, Xin |
| contents | Echocardiography plays an important role in the screening and diagnosis of cardiovascular diseases. However, automated intelligent analysis of echocardiographic data remains challenging due to complex cardiac dynamics and strong view heterogeneity. In recent years, visual language models (VLM) have opened a new avenue for building ultrasound understanding systems for clinical decision support. Nevertheless, most existing methods formulate this task as a direct mapping from video and question to answer, making them vulnerable to template shortcuts and spurious explanations. To address these issues, we propose EchoTrust, an evidence-driven Actor-Verifier framework for trustworthy reasoning in echocardiography VLM-based agents. EchoTrust produces a structured intermediate representation that is subsequently analyzed by distinct roles, enabling more reliable and interpretable decision-making for high-stakes clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06347 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agents Huang, Peng Wang, Yiming Chen, Yineng Gui, Liangqiao Guo, Hui Peng, Bo Hu, Shu Wu, Xi Connie, Tsao Zhu, Hongtu Prabhakaran, Balakrishnan Wang, Xin Computer Vision and Pattern Recognition Echocardiography plays an important role in the screening and diagnosis of cardiovascular diseases. However, automated intelligent analysis of echocardiographic data remains challenging due to complex cardiac dynamics and strong view heterogeneity. In recent years, visual language models (VLM) have opened a new avenue for building ultrasound understanding systems for clinical decision support. Nevertheless, most existing methods formulate this task as a direct mapping from video and question to answer, making them vulnerable to template shortcuts and spurious explanations. To address these issues, we propose EchoTrust, an evidence-driven Actor-Verifier framework for trustworthy reasoning in echocardiography VLM-based agents. EchoTrust produces a structured intermediate representation that is subsequently analyzed by distinct roles, enabling more reliable and interpretable decision-making for high-stakes clinical applications. |
| title | Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agents |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.06347 |