Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation

Sixing Yan*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationBioNLP 2022 @ ACL 2022 - Proceedings of the 21st Workshop on Biomedical Language Processing
EditorsDina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
PublisherAssociation for Computational Linguistics (ACL)
Pages116-122
Number of pages7
ISBN (Electronic)9781955917278
DOIs
Publication statusPublished - May 2022
Event21st Workshop on Biomedical Language Processing, BioNLP 2022 at the Association for Computational Linguistics Conference, ACL 2022 - Dublin, Ireland
Duration: 26 May 2022 → …

Publication series

NameProceedings of the Workshop on Biomedical Language Processing

Conference

Conference21st Workshop on Biomedical Language Processing, BioNLP 2022 at the Association for Computational Linguistics Conference, ACL 2022
Country/TerritoryIreland
CityDublin
Period26/05/22 → …

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