用于纵向胸部X光报告的过渡感知最佳N采样

摘要

在纵向临床实践中，每张胸部X光片的阅读都需结合患者既往检查结果，放射科医师报告的核心内容通常是患者两轮就诊间的变化。据我们所知，本文首次提出了一种免训练的最佳N采样方案，该方案专为预训练的胸部X光报告生成模型设计，显式地利用了从既往到当前检查的纵向变化先验信息。我们将其称为“感知变化的最佳N采样”：首先将每份报告切分为句子，并嵌入为R^d空间中的无序集合；随后通过一种专门编码两集合间变化的集合间距离，将每对（既往、当前）检查数据转化为固定维度的方向向量；最后通过候选转换向量与缓存的真实训练转换向量库（以最小值或k近邻方式聚合）之间的余弦距离对候选报告进行评分。我们使用四种方向性集合距离（均值偏移、新颖性残差、定向Hausdorff锚点、代价加权最优传输）实例化该框架，并在多轮AP-PA队列上，通过三种提示对三个视觉-语言生成模型进行推理评估。感知变化的最佳N采样在所有评估场景下均优于随机选择，其中在“印象”部分的相对提升最为显著。

English

In longitudinal clinical practice, every chest X-ray is read in the context of the patients prior exam, and much of what the radiologist communicates is the change from one visit to the next. To the best of our knowledge, we present the first training-free best-of-N sampling scheme for pre-trained chest X-ray report generators that is explicitly aware of this longitudinal prior to current transition. We call it transition-aware best-of-N sampling, each report is split into sentences and embedded into an unordered set in Rd; each (prior, current) pair is reduced to a fixed-dim directional vector via a set-to-set distance designed to encode the change between the two sets; and candidates are scored by cosine distance from their candidate transition vector to a cached bank of ground-truth training transition vectors, aggregated as min or kNN. We instantiate the framework with four directional set distances (mean-shift, novelty residual, directed-Hausdorff anchor, and cost-weighted optimal transport) and evaluate on a multi-visit AP-PA cohort, running inference under three prompts on three vision-language generators. Transition-aware best-of-N outperforms random selection across the board, with the largest relative gains on the Impression section.