Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation

Xingyi Yang, Muchao Ye, Quanzeng You, Fenglong Ma


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.
Anthology ID:
2021.acl-long.387
Volume:
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:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5000–5009
Language:
URL:
https://aclanthology.org/2021.acl-long.387
DOI:
10.18653/v1/2021.acl-long.387
Bibkey:
Cite (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.
Cite (Informal):
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation (Yang et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.387.pdf
Video:
 https://aclanthology.org/2021.acl-long.387.mp4
Data
MIMIC-CXR