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.

本文提出了一种新的方法，通过使用标签机从医学报告中提取比较先前的信息，并将该先前信息整合到基于 Transformer 的模型中，从而更加真实和全面地生成医学报告。该方法测试表明效果优于之前的最先进模型，提供了一个有前景的方向来弥补医学报告生成中放射科医生和模型之间的知识差距。