@inproceedings{kim-etal-2023-boosting,
title = "Boosting Radiology Report Generation by Infusing Comparison Prior",
author = "Kim, Sanghwan and
Nooralahzadeh, Farhad and
Rohanian, Morteza and
Fujimoto, Koji and
Nishio, Mizuho and
Sakamoto, Ryo and
Rinaldi, Fabio and
Krauthammer, Michael",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.4",
doi = "10.18653/v1/2023.bionlp-1.4",
pages = "50--61",
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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<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.</abstract>
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%0 Conference Proceedings
%T Boosting Radiology Report Generation by Infusing Comparison Prior
%A Kim, Sanghwan
%A Nooralahzadeh, Farhad
%A Rohanian, Morteza
%A Fujimoto, Koji
%A Nishio, Mizuho
%A Sakamoto, Ryo
%A Rinaldi, Fabio
%A Krauthammer, Michael
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kim-etal-2023-boosting
%X 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.
%R 10.18653/v1/2023.bionlp-1.4
%U https://aclanthology.org/2023.bionlp-1.4
%U https://doi.org/10.18653/v1/2023.bionlp-1.4
%P 50-61
Markdown (Informal)
[Boosting Radiology Report Generation by Infusing Comparison Prior](https://aclanthology.org/2023.bionlp-1.4) (Kim et al., BioNLP 2023)
ACL
- Sanghwan Kim, Farhad Nooralahzadeh, Morteza Rohanian, Koji Fujimoto, Mizuho Nishio, Ryo Sakamoto, Fabio Rinaldi, and Michael Krauthammer. 2023. Boosting Radiology Report Generation by Infusing Comparison Prior. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 50–61, Toronto, Canada. Association for Computational Linguistics.