Cross-modal Memory Networks for Radiology Report Generation

Zhihong Chen, Yaling Shen, Yan Song, Xiang Wan


Abstract
Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments. By generating the reports automatically, it is beneficial to help lighten the burden of radiologists and significantly promote clinical automation, which already attracts much attention in applying artificial intelligence to medical domain. Previous studies mainly follow the encoder-decoder paradigm and focus on the aspect of text generation, with few studies considering the importance of cross-modal mappings and explicitly exploit such mappings to facilitate radiology report generation. In this paper, we propose a cross-modal memory networks (CMN) to enhance the encoder-decoder framework for radiology report generation, where a shared memory is designed to record the alignment between images and texts so as to facilitate the interaction and generation across modalities. Experimental results illustrate the effectiveness of our proposed model, where state-of-the-art performance is achieved on two widely used benchmark datasets, i.e., IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is able to better align information from radiology images and texts so as to help generating more accurate reports in terms of clinical indicators.
Anthology ID:
2021.acl-long.459
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
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5904–5914
Language:
URL:
https://aclanthology.org/2021.acl-long.459
DOI:
10.18653/v1/2021.acl-long.459
Bibkey:
Cite (ACL):
Zhihong Chen, Yaling Shen, Yan Song, and Xiang Wan. 2021. Cross-modal Memory Networks for Radiology 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 5904–5914, Online. Association for Computational Linguistics.
Cite (Informal):
Cross-modal Memory Networks for Radiology Report Generation (Chen et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.459.pdf
Video:
 https://aclanthology.org/2021.acl-long.459.mp4
Code
 zhjohnchan/R2GenCMN
Data
CheXpert