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Radiology report generation aims to automatically generate a clinicallyaccurate and coherent paragraph from the X-ray image, which could relieveradiologists from the heavy burden of report writing. Although various imagecaption methods have shown remarkable performance in the natural image field,generating accurate reports for medical images requires knowledge of multiplemodalities, including vision, language, and medical terminology. We propose aKnowledge-injected U-Transformer (KiUT) to learn multi-level visualrepresentation and adaptively distill the information with contextual andclinical knowledge for word prediction. In detail, a U-connection schemabetween the encoder and decoder is designed to model interactions betweendifferent modalities. And a symptom graph and an injected knowledge distillerare developed to assist the report generation. Experimentally, we outperformstate-of-the-art methods on two widely used benchmark datasets: IU-Xray andMIMIC-CXR. Further experimental results prove the advantages of ourarchitecture and the complementary benefits of the injected knowledge.

KiUT: Knowledge-injected U-Transformer for Radiology Report Generation