Boosting Radiology Report Generation by Infusing Comparison Prior

• URL: http://arxiv.org/abs/2305.04561v2
• Date: Mon, 5 Jun 2023 10:28:11 GMT
• Title: Boosting Radiology Report Generation by Infusing Comparison Prior
• Authors: Sanghwan Kim, Farhad Nooralahzadeh, Morteza Rohanian, Koji Fujimoto, Mizuho Nishio, Ryo Sakamoto, Fabio Rinaldi, and Michael Krauthammer
• Abstract summary: Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. These models often lack prior knowledge, resulting in the generation of synthetic reports that mistakenly reference non-existent prior exams. We propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports.
• Score: 7.054671146863795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often lack prior knowledge, resulting in the generation of synthetic reports that mistakenly reference non-existent prior exams. This discrepancy can be attributed to a knowledge gap between radiologists and the generation models. While radiologists possess patient-specific prior information, the models solely receive X-ray images at a specific time point. To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports. This extracted comparison prior is then seamlessly integrated into state-of-the-art transformer-based models, enabling them to produce more realistic and comprehensive reports. Our method is evaluated on English report datasets, such as IU X-ray and MIMIC-CXR. The results demonstrate that our approach surpasses baseline models in terms of natural language generation metrics. Notably, our model generates reports that are free from false references to non-existent prior exams, setting it apart from previous models. By addressing this limitation, our approach represents a significant step towards bridging the gap between radiologists and generation models in the domain of medical report generation.

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