English(EN) Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation

新的CORAL框架通过可解释的推理增强了医学报告生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了CORAL，一个新颖的多模态框架，旨在提高从医学影像数据生成报告的可解释性和准确性。该框架集成了空间定位和概念级监督，实现了更符合临床的推理过程。CORAL利用提示驱动的分割模型进行病灶定位，并使用概念瓶颈模块预测临床属性，然后将这些信息输入多模态大语言模型（MLLM）以生成结构化报告和诊断。在BUS-CoT和IU X-ray数据集上的实验表明，CORAL在诊断准确性和报告质量方面优于现有的MLLM。 AI

影响通过将推理与临床概念联系起来，增强了医学AI的可解释性和准确性。

排序理由该集群包含一篇详细介绍医学报告生成新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Xinyue Xu, Hongbin Lin, Juangui Xu, Hualiang Wang, Lehan Wang, Lijie Hu, Weiyang Liu, Adrian Weller, Xiaomeng Li · 2026-09-15 04:00

面向可解释结构化报告生成的概念驱动的提示驱动本地化推理

arXiv:2609.15334v1 Announce Type: cross Abstract: Medical imaging modalities such as ultrasound and X-ray are widely used in clinical practice, where diagnosis follows a structured, evidence-driven workflow aligned with standardized criteria. While multimodal large language model…

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报道来源 [1]

面向可解释结构化报告生成的概念驱动的提示驱动本地化推理

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