RadHiera：面向医学报告生成的语义分层强化学习
RadHiera: Semantic Hierarchical Reinforcement Learning for Medical Report Generation
AI总结：
本文提出RadHiera框架，通过分层强化学习提升医学报告生成的语义一致性与诊断准确性，实验表明其在胸部X光和超声报告生成中表现优异。
摘要：
视觉-语言模型在放射学报告生成中展现出良好效果。然而，现有方法多生成扁平文本，未显式建模发现与结论部分的语义依赖，导致临床观察与诊断结论不一致。本文提出RadHiera，一种面向放射学报告生成的语义分层强化学习框架。RadHiera遵循放射学报告的语义结构，首先优化整体报告质量，然后提升结论部分的诊断准确性，最后强制发现与结论之间的一致性，使诊断结论有临床证据支持。具体而言，我们首先结合语言质量和医学事实性构建基础奖励，为整个报告提供监督。在此基础上，引入针对严重程度的奖励机制，重点减少涉及临床关键状况的错误，从而降低漏诊和过度诊断。我们进一步利用专家模型导出的标签集强制跨部分一致性，通过子集约束和幻觉惩罚确保结论忠实于发现。在三个公开的胸部X光基准测试中，RadHiera在诊断准确性和跨部分一致性方面优于现有方法，同时在超声报告生成中也表现出良好的适应性。
英文摘要：
Vision-language models have shown promising results in radiology report generation. However, most existing methods generate reports as flat text and do not explicitly model the semantic dependency between the Findings and Impression sections, which can lead to inconsistencies between clinical observations and diagnostic conclusions. In this paper, we propose RadHiera, a semantic hierarchical reinforcement learning framework for radiology report generation. RadHiera follows the semantic organization of radiology reports by first optimizing overall report quality, then improving the diagnostic accuracy of the Impression section, and finally enforcing consistency between Findings and Impression so that diagnostic conclusions are supported by clinical evidence. Specifically, we begin with a base reward that combines linguistic quality and medical factuality to provide supervision on the whole report. On this basis, we introduce a severity-aware reward for the Impression section that places greater emphasis on errors involving clinically critical conditions, thereby reducing both missed diagnoses and overstatement. We further enforce cross-section consistency using Expert Model-derived label sets, with subset constraints and hallucination penalties to ensure that impressions remain faithful to the findings. Experiments on three public chest X-ray benchmarks show that RadHiera consistently improves diagnostic accuracy and inter-section consistency over state-of-the-art methods, while also demonstrating good adaptability to report generation in ultrasound report generation.
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