@inproceedings{yan-etal-2023-style,
title = "Style-Aware Radiology Report Generation with {R}ad{G}raph and Few-Shot Prompting",
author = "Yan, Benjamin and
Liu, Ruochen and
Kuo, David and
Adithan, Subathra and
Reis, Eduardo and
Kwak, Stephen and
Venugopal, Vasantha and
O{'}Connell, Chloe and
Saenz, Agustina and
Rajpurkar, Pranav and
Moor, Michael",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.977",
doi = "10.18653/v1/2023.findings-emnlp.977",
pages = "14676--14688",
abstract = "Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generating a fully-fledged report from an image. However, this conflates the content of the report (e.g., findings and their attributes) with its style (e.g., format and choice of words), which can lead to clinically inaccurate reports. To address this, we propose a two-step approach for radiology report generation. First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist. For this, we leverage RadGraph{---}a graph representation of reports{---}together with large language models (LLMs). In our quantitative evaluations, we find that our approach leads to beneficial performance. Our human evaluation with clinical raters highlights that the AI-generated reports are indistinguishably tailored to the style of individual radiologist despite leveraging only a few examples as context.",
}
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<abstract>Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generating a fully-fledged report from an image. However, this conflates the content of the report (e.g., findings and their attributes) with its style (e.g., format and choice of words), which can lead to clinically inaccurate reports. To address this, we propose a two-step approach for radiology report generation. First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist. For this, we leverage RadGraph—a graph representation of reports—together with large language models (LLMs). In our quantitative evaluations, we find that our approach leads to beneficial performance. Our human evaluation with clinical raters highlights that the AI-generated reports are indistinguishably tailored to the style of individual radiologist despite leveraging only a few examples as context.</abstract>
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%0 Conference Proceedings
%T Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
%A Yan, Benjamin
%A Liu, Ruochen
%A Kuo, David
%A Adithan, Subathra
%A Reis, Eduardo
%A Kwak, Stephen
%A Venugopal, Vasantha
%A O’Connell, Chloe
%A Saenz, Agustina
%A Rajpurkar, Pranav
%A Moor, Michael
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yan-etal-2023-style
%X Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generating a fully-fledged report from an image. However, this conflates the content of the report (e.g., findings and their attributes) with its style (e.g., format and choice of words), which can lead to clinically inaccurate reports. To address this, we propose a two-step approach for radiology report generation. First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist. For this, we leverage RadGraph—a graph representation of reports—together with large language models (LLMs). In our quantitative evaluations, we find that our approach leads to beneficial performance. Our human evaluation with clinical raters highlights that the AI-generated reports are indistinguishably tailored to the style of individual radiologist despite leveraging only a few examples as context.
%R 10.18653/v1/2023.findings-emnlp.977
%U https://aclanthology.org/2023.findings-emnlp.977
%U https://doi.org/10.18653/v1/2023.findings-emnlp.977
%P 14676-14688
Markdown (Informal)
[Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting](https://aclanthology.org/2023.findings-emnlp.977) (Yan et al., Findings 2023)
ACL
- Benjamin Yan, Ruochen Liu, David Kuo, Subathra Adithan, Eduardo Reis, Stephen Kwak, Vasantha Venugopal, Chloe O’Connell, Agustina Saenz, Pranav Rajpurkar, and Michael Moor. 2023. Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14676–14688, Singapore. Association for Computational Linguistics.