@inproceedings{yang-etal-2021-writing,
title = "Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation",
author = "Yang, Xingyi and
Ye, Muchao and
You, Quanzeng and
Ma, Fenglong",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.387",
doi = "10.18653/v1/2021.acl-long.387",
pages = "5000--5009",
abstract = "Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.",
}
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<abstract>Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.</abstract>
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%0 Conference Proceedings
%T Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
%A Yang, Xingyi
%A Ye, Muchao
%A You, Quanzeng
%A Ma, Fenglong
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F yang-etal-2021-writing
%X Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.
%R 10.18653/v1/2021.acl-long.387
%U https://aclanthology.org/2021.acl-long.387
%U https://doi.org/10.18653/v1/2021.acl-long.387
%P 5000-5009
Markdown (Informal)
[Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation](https://aclanthology.org/2021.acl-long.387) (Yang et al., ACL-IJCNLP 2021)
ACL
- Xingyi Yang, Muchao Ye, Quanzeng You, and Fenglong Ma. 2021. Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5000–5009, Online. Association for Computational Linguistics.