Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation
Sixing Yan · 2022
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.We carry out extensive experiments and show that the proposed MaKG deep model can improve the clinical accuracy of the generated reports.