Boosting Radiology Report Generation by Infusing Comparison Prior

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.