@inproceedings{cho-etal-2025-crepe,
title = "{CREPE}: Rapid Chest {X}-ray Report Evaluation by Predicting Multi-category Error Counts",
author = "Cho, Gihun and
Jang, Seunghyun and
Ko, Hanbin and
Baek, Inhyeok and
Park, Chang Min",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1102/",
doi = "10.18653/v1/2025.emnlp-main.1102",
pages = "21738--21755",
ISBN = "979-8-89176-332-6",
abstract = "We introduce CREPE (Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts), a rapid, interpretable, and clinically grounded metric for automated chest X-ray report generation. CREPE uses a domain-specific BERT model fine-tuned with a multi-head regression architecture to predict error counts across six clinically meaningful categories. Trained on a large-scale synthetic dataset of 32,000 annotated report pairs, CREPE demonstrates strong generalization and interpretability. On the expert-annotated ReXVal dataset, CREPE achieves a Kendall{'}s tau correlation of 0.786 with radiologist error counts, outperforming traditional and recent metrics. CREPE achieves these results with an inference speed approximately 280 times faster than large language model (LLM)-based approaches, enabling rapid and fine-grained evaluation for scalable development of chest X-ray report generation models."
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<abstract>We introduce CREPE (Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts), a rapid, interpretable, and clinically grounded metric for automated chest X-ray report generation. CREPE uses a domain-specific BERT model fine-tuned with a multi-head regression architecture to predict error counts across six clinically meaningful categories. Trained on a large-scale synthetic dataset of 32,000 annotated report pairs, CREPE demonstrates strong generalization and interpretability. On the expert-annotated ReXVal dataset, CREPE achieves a Kendall’s tau correlation of 0.786 with radiologist error counts, outperforming traditional and recent metrics. CREPE achieves these results with an inference speed approximately 280 times faster than large language model (LLM)-based approaches, enabling rapid and fine-grained evaluation for scalable development of chest X-ray report generation models.</abstract>
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%0 Conference Proceedings
%T CREPE: Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts
%A Cho, Gihun
%A Jang, Seunghyun
%A Ko, Hanbin
%A Baek, Inhyeok
%A Park, Chang Min
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F cho-etal-2025-crepe
%X We introduce CREPE (Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts), a rapid, interpretable, and clinically grounded metric for automated chest X-ray report generation. CREPE uses a domain-specific BERT model fine-tuned with a multi-head regression architecture to predict error counts across six clinically meaningful categories. Trained on a large-scale synthetic dataset of 32,000 annotated report pairs, CREPE demonstrates strong generalization and interpretability. On the expert-annotated ReXVal dataset, CREPE achieves a Kendall’s tau correlation of 0.786 with radiologist error counts, outperforming traditional and recent metrics. CREPE achieves these results with an inference speed approximately 280 times faster than large language model (LLM)-based approaches, enabling rapid and fine-grained evaluation for scalable development of chest X-ray report generation models.
%R 10.18653/v1/2025.emnlp-main.1102
%U https://aclanthology.org/2025.emnlp-main.1102/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1102
%P 21738-21755
Markdown (Informal)
[CREPE: Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts](https://aclanthology.org/2025.emnlp-main.1102/) (Cho et al., EMNLP 2025)
ACL