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Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation
Proceedings of the 21st Workshop on Biomedical Language Processing
Automatic generating the clinically accurate radiology report from X-ray images is important but challenging. The identification of multigrained abnormal regions in image and corresponding abnormalities is difficult for datadriven 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. Wedoi:10.18653/v1/2022.bionlp-1.11 fatcat:t4wfuqdhxrfxnhfn7knv6ximoe