Radiology Report Generation (RRG) has achieved significant progress with the advancements of multimodal generative models. However, the evaluation in the domain suffers from a lack of fair and robust metrics. We reveal that, high performance on RRG with existing lexical-based metrics (e.g. BLEU) might be more of a mirage - a model can get a high BLEU only by learning the template of reports. This has become an urgent problem for RRG due to the highly patternized nature of these reports. In this work, we un-intuitively approach this problem by proposing the Layman's RRG framework, a layman's terms-based dataset, evaluation and training framework that systematically improves RRG with day-to-day language. We first contribute the translated Layman's terms dataset. Building upon the dataset, we then propose a semantics-based evaluation method, which is proved to mitigate the inflated numbers of BLEU and provides fairer evaluation. Last, we show that training on the layman's terms dataset encourages models to focus on the semantics of the reports, as opposed to overfitting to learning the report templates. We reveal a promising scaling law between the number of training examples and semantics gain provided by our dataset, compared to the inverse pattern brought by the original formats. Our code is available at \url{https://github.com/hegehongcha/LaymanRRG}.

通过引入一个以外行人术语为基础的数据集、评估和训练框架来系统地改进放射学报告生成(RRG)，以解决现有用于评估RRG的词汇基准度量方法（如BLEU）的问题，该方法的高性能可能只是一个幻觉。我们提出了一个基于语义的评估方法，并证明其可以缓解BLEU得分的虚高问题，为RRG提供更公平的评估。我们还展示了在外行人术语数据集上训练可以促使模型关注报告的语义，而不是过度拟合报告模板。

X射线简化：用通俗语言生成和评估放射学报告