Abstract
Automatic generating the clinically accurate radiology report from X-ray images is important but challenging. The identification of multi-grained abnormal regions in image and corresponding abnormalities is difficult for data-driven neural models. In this work, we introduce a Memory-aligned Knowledge Graph (MaKG) of clinical abnormalities to better learn the visual patterns of abnormalities and their relationships by integrating it into a deep model architecture for the report generation. We carry out extensive experiments and show that the proposed MaKG deep model can improve the clinical accuracy of the generated reports.- Anthology ID:
- 2022.bionlp-1.11
- Volume:
- Proceedings of the 21st Workshop on Biomedical Language Processing
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 116–122
- Language:
- URL:
- https://aclanthology.org/2022.bionlp-1.11
- DOI:
- 10.18653/v1/2022.bionlp-1.11
- Cite (ACL):
- Sixing Yan. 2022. Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 116–122, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation (Yan, BioNLP 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.bionlp-1.11.pdf