Prior Knowledge Enhances Radiology Report Generation
This work addresses the bottleneck of improving radiology report generation for radiologists, but it is incremental as it builds on existing deep learning methods with a specific enhancement.
The paper tackled the problem of generating radiology reports by addressing the neglect of mutual influences between medical findings, which limits report quality, and achieved a ROUGE-L of 0.384 and CIDEr of 0.340 on the IU X-ray dataset, with an average 1.6% improvement over previous works.
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.