@inproceedings{yan-etal-2021-weakly-supervised,
title = "Weakly Supervised Contrastive Learning for Chest {X}-Ray Report Generation",
author = "Yan, An and
He, Zexue and
Lu, Xing and
Du, Jiang and
Chang, Eric and
Gentili, Amilcare and
McAuley, Julian and
Hsu, Chun-Nan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.336",
doi = "10.18653/v1/2021.findings-emnlp.336",
pages = "4009--4015",
abstract = "Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets. To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation. Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones. It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.",
}
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<abstract>Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets. To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation. Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones. It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.</abstract>
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%0 Conference Proceedings
%T Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
%A Yan, An
%A He, Zexue
%A Lu, Xing
%A Du, Jiang
%A Chang, Eric
%A Gentili, Amilcare
%A McAuley, Julian
%A Hsu, Chun-Nan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F yan-etal-2021-weakly-supervised
%X Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets. To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation. Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones. It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.
%R 10.18653/v1/2021.findings-emnlp.336
%U https://aclanthology.org/2021.findings-emnlp.336
%U https://doi.org/10.18653/v1/2021.findings-emnlp.336
%P 4009-4015
Markdown (Informal)
[Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation](https://aclanthology.org/2021.findings-emnlp.336) (Yan et al., Findings 2021)
ACL
- An Yan, Zexue He, Xing Lu, Jiang Du, Eric Chang, Amilcare Gentili, Julian McAuley, and Chun-Nan Hsu. 2021. Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4009–4015, Punta Cana, Dominican Republic. Association for Computational Linguistics.