Generating Radiology Reports via Memory-driven Transformer

Zhihong Chen, Yan Song, Tsung-Hui Chang, Xiang Wan


Abstract
Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment. Writing imaging reports is time-consuming and can be error-prone for inexperienced radiologists. Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is an essential task to apply artificial intelligence to the medical domain. In this paper, we propose to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the generation process and a memory-driven conditional layer normalization is applied to incorporating the memory into the decoder of Transformer. Experimental results on two prevailing radiology report datasets, IU X-Ray and MIMIC-CXR, show that our proposed approach outperforms previous models with respect to both language generation metrics and clinical evaluations. Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge. Further analyses also demonstrate that our approach is able to generate long reports with necessary medical terms as well as meaningful image-text attention mappings.
Anthology ID:
2020.emnlp-main.112
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1439–1449
Language:
URL:
https://aclanthology.org/2020.emnlp-main.112
DOI:
10.18653/v1/2020.emnlp-main.112
Bibkey:
Cite (ACL):
Zhihong Chen, Yan Song, Tsung-Hui Chang, and Xiang Wan. 2020. Generating Radiology Reports via Memory-driven Transformer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1439–1449, Online. Association for Computational Linguistics.
Cite (Informal):
Generating Radiology Reports via Memory-driven Transformer (Chen et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.112.pdf
Video:
 https://slideslive.com/38938877
Code
 zhjohnchan/R2Gen +  additional community code
Data
MIMIC-CXR