  Automatic radiology report generation is critical in clinics which can
relieve experienced radiologists from the heavy workload and remind
inexperienced radiologists of misdiagnosis or missed diagnose. Existing
approaches mainly formulate radiology report generation as an image captioning
task and adopt the encoder-decoder framework. However, in the medical domain,
such pure data-driven approaches suffer from the following problems: 1) visual
and textual bias problem; 2) lack of expert knowledge. In this paper, we
propose a knowledge-enhanced radiology report generation approach introduces
two types of medical knowledge: 1) General knowledge, which is input
independent and provides the broad knowledge for report generation; 2) Specific
knowledge, which is input dependent and provides the fine-grained knowledge for
report generation. To fully utilize both the general and specific knowledge, we
also propose a knowledge-enhanced multi-head attention mechanism. By merging
the visual features of the radiology image with general knowledge and specific
knowledge, the proposed model can improve the quality of generated reports.
Experimental results on two publicly available datasets IU-Xray and MIMIC-CXR
show that the proposed knowledge enhanced approach outperforms state-of-the-art
image captioning based methods. Ablation studies also demonstrate that both
general and specific knowledge can help to improve the performance of radiology
report generation.
