Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards

Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz


Abstract
Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. These systems have achieved promising performance as measured by widely used NLG metrics such as BLEU and CIDEr. However, the current systems face important limitations. First, they present an increased complexity in architecture that offers only marginal improvements on NLG metrics. Secondly, these systems that achieve high performance on these metrics are not always factually complete or consistent due to both inadequate training and evaluation. Recent studies have shown the systems can be substantially improved by using new methods encouraging 1) the generation of domain entities consistent with the reference and 2) describing these entities in inferentially consistent ways. So far, these methods rely on weakly-supervised approaches (rule-based) and named entity recognition systems that are not specific to the chest X-ray domain. To overcome this limitation, we propose a new method, the RadGraph reward, to further improve the factual completeness and correctness of generated radiology reports. More precisely, we leverage the RadGraph dataset containing annotated chest X-ray reports with entities and relations between entities. On two open radiology report datasets, our system substantially improves the scores up to 14.2% and 25.3% on metrics evaluating the factual correctness and completeness of reports.
Anthology ID:
2022.findings-emnlp.319
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4348–4360
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.319
DOI:
10.18653/v1/2022.findings-emnlp.319
Bibkey:
Cite (ACL):
Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, and Curtis Langlotz. 2022. Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4348–4360, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (Delbrouck et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.319.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.319.mp4