  Radiology report generation aims to produce computer-aided diagnoses to
alleviate the workload of radiologists and has drawn increasing attention
recently. However, previous deep learning methods tend to neglect the mutual
influences between medical findings, which can be the bottleneck that limits
the quality of generated reports. In this work, we propose to mine and
represent the associations among medical findings in an informative knowledge
graph and incorporate this prior knowledge with radiology report generation to
help improve the quality of generated reports. Experiment results demonstrate
the superior performance of our proposed method on the IU X-ray dataset with a
ROUGE-L of 0.384$\pm$0.007 and CIDEr of 0.340$\pm$0.011. Compared with previous
works, our model achieves an average of 1.6% improvement (2.0% and 1.5%
improvements in CIDEr and ROUGE-L, respectively). The experiments suggest that
prior knowledge can bring performance gains to accurate radiology report
generation. We will make the code publicly available at
https://github.com/bionlplab/report_generation_amia2022.
