基于检索的生成用于更安全的病理科图像描述生成
Retrieval-Guided Generation for Safer Histopathology Image Captioning
AI总结：
本文提出检索引导生成方法，通过总结相似病例的专家文本生成描述，提升病理图像描述的准确性与可靠性，实验表明其在语义对齐和诊断一致性方面优于现有方法。
摘要：
生成性视觉-语言模型能够生成流畅的医学图像描述，但容易产生幻觉、过度具体的诊断断言和事实不一致等问题。本文研究了检索引导生成（RGG）作为更安全的替代方案，通过总结视觉相似病例的专家文本生成描述，而非从头生成。在ARCH病理科数据集上，RGG在语义对齐方面优于MedGemma，达到约0.60的余弦相似度，而非重叠置信区间表明稳健提升。由病理学家主导的定性审查显示，RGG在保留形态相关术语和减少不支持诊断方面表现更好，但揭示了概念混合和继承性过度具体标注等失败模式。总体而言，检索引导描述生成提供了一种更透明和可靠的途径，比完全生成方法有更清晰的审计机会。
英文摘要：
Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and factual inconsistency-serious issues in pathology. We investigate retrieval-guided generation (RGG) as a safer alternative, where captions are formed by summarizing expert text from visually similar cases rather than generated de novo. On the ARCH histopathology dataset, RGG improves semantic alignment with ground truth, achieving cosine similarity of $\approx$0.60 versus $\approx$0.47 from MedGemma, with non-overlapping confidence intervals indicating a robust gain. A pathologist-led qualitative review shows better preservation of morphology-relevant terminology and fewer unsupported diagnoses, while revealing failure modes such as concept mixing and inherited over-specific labeling. Overall, retrieval-guided captioning offers a more transparent and reliable approach with clearer opportunities for auditing than fully generative methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
