@inproceedings{yan-2022-memory,
title = "Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation",
author = "Yan, Sixing",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.bionlp-1.11",
doi = "10.18653/v1/2022.bionlp-1.11",
pages = "116--122",
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.",
}
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%0 Conference Proceedings
%T Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation
%A Yan, Sixing
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 21st Workshop on Biomedical Language Processing
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F yan-2022-memory
%X 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.
%R 10.18653/v1/2022.bionlp-1.11
%U https://aclanthology.org/2022.bionlp-1.11
%U https://doi.org/10.18653/v1/2022.bionlp-1.11
%P 116-122
Markdown (Informal)
[Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation](https://aclanthology.org/2022.bionlp-1.11) (Yan, BioNLP 2022)
ACL