BiOTPrompt：双向最优传输引导的疾病演化感知放射报告生成
BiOTPrompt: Bidirectional Optimal Transport Guided Prompting for Disease Evolution-aware Radiology Report Generation
用双向最优传输计划的不对称性来定位新出现与已消退病灶，这一思路可迁移到任何需要比较两期影像差异的视觉任务（如变化检测、随访影像分析）；同时其视觉-语言一致性约束为缓解多模态生成中的幻觉提供了可复用的正则手段。
原文依据 By analyzing the asymmetry between bidirectional transport plans, BiOTPrompt can identify newly emerged and resolved regions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论放射报告生成（RRG）旨在通过自由文本报告自动描述医学图像。在临床实践中，对比当前与既往胸部X光片对于评估疾病进展至关重要，这推动了纵向RRG方法的发展。然而，现有方法大多难以捕捉细粒度的时序变化，因为它们往往依赖单向对齐或静态推理流程，忽略了疾病演化的双向与非对称本质。为应对这些挑战，我们提出BiOTPrompt，一个面向疾病演化感知放射报告生成的新框架，它引入双向最优传输（BiOT）机制来显式建模历史与当前胸部X光片之间的进展动态。通过分析双向传输计划之间的不对称性，BiOTPrompt能够识别新出现与已消退的区域，并据此构建动态提示，引导大语言模型（LLM）生成具有临床相关性的诊断报告。此外，我们引入视觉-语言一致性约束，以确保视觉证据与文本描述之间的对齐，从而缓解幻觉并提升事实正确性。在Longitudinal-MIMIC数据集上的大量实验表明，BiOTPrompt在语言指标与临床相关性两方面均达到最优性能，为纵向放射报告生成树立了新的标准。
英文原文摘要 1502 字符
Radiology report generation (RRG) aims to automatically describe medical images via free-text reports. In clinical practice, comparing current and prior chest X-rays is essential for assessing disease progression, motivating the development of longitudinal RRG methods. However, most existing approaches often struggle to capture fine-grained temporal changes, as they often rely on unidirectional alignments or static reasoning pipelines, overlooking the bidirectional and asymmetric nature of disease evolution. To tackle these challenges, we propose BiOTPrompt, a novel framework for disease evolution-aware radiology report generation, which introduces a Bidirectional Optimal Transport (BiOT) mechanism to explicitly model progression dynamics between historical and current chest X-rays. By analyzing the asymmetry between bidirectional transport plans, BiOTPrompt can identify newly emerged and resolved regions, which are then used to construct dynamic prompts that guide large language models (LLMs) in generating clinically relevant diagnostic reports. Furthermore, we incorporate a vision-language consistency constraint to ensure alignment between visual evidence and textual descriptions, mitigating hallucinations and enhancing factual correctness. Extensive experiments on the Longitudinal-MIMIC dataset demonstrate that BiOTPrompt achieves state-of-the-art performance in both language metrics and clinical relevance, setting a new standard for longitudinal radiology report generation.