Current transformer-based models achieved great success in generating radiology reports from chest X-ray images. Nonetheless, one of the major issues is the model's lack of prior knowledge, which frequently leads to false references to non-existent prior exams in synthetic reports. This is mainly due to the knowledge gap between radiologists and the generation models: radiologists are aware of the prior information of patients to write a medical report, while models only receive X-ray images at a specific time. To address this issue, we propose a novel approach that employs a labeler to extract comparison prior information from radiology reports in the IU X-ray and MIMIC-CXR datasets. This comparison prior is then incorporated into state-of-the-art transformer-based models, allowing them to generate more realistic and comprehensive reports. We test our method on the IU X-ray and MIMIC-CXR datasets and find that it outperforms previous state-of-the-art models in terms of both automatic and human evaluation metrics. In addition, unlike previous models, our model generates reports that do not contain false references to non-existent prior exams. Our approach provides a promising direction for bridging the gap between radiologists and generation models in medical report generation.

翻译：当前基于Transformer的模型在从胸部X光图像生成放射学报告方面取得了巨大成功。然而，一个主要问题是模型缺乏先验知识，这常导致合成报告中错误地引用不存在的既往检查。这主要源于放射科医生与生成模型之间的知识鸿沟：放射科医生撰写医疗报告时掌握患者的先验信息，而模型仅能获取特定时刻的X光图像。为解决该问题，我们提出了一种新方法，通过标注器从IU X-ray和MIMIC-CXR数据集的放射学报告中提取比较先验信息。随后将该比较先验信息注入最先进的Transformer模型，使其能生成更真实全面的报告。我们在IU X-ray和MIMIC-CXR数据集上进行了测试，结果表明，无论自动评估还是人工评价指标，该模型均优于先前最先进模型。此外，与先前模型不同，我们的模型生成的报告不会错误引用不存在的既往检查。该方法为弥合放射科医生与生成模型在医学报告生成中的差距提供了有前景的方向。