论文标题
先验知识增强了放射学报告的生成
Prior Knowledge Enhances Radiology Report Generation
论文作者
论文摘要
放射学报告的生成旨在产生计算机辅助诊断以减轻放射科医生的工作量，并最近引起了人们的关注。但是，以前的深度学习方法倾向于忽略医疗发现之间的相互影响，这可能是限制生成报告质量的瓶颈。在这项工作中，我们建议在内容丰富的知识图中挖掘并代表医学发现之间的关联，并将这一先验知识与放射学报告的生成结合在一起，以帮助提高生成的报告的质量。实验结果证明了我们在IU X射线数据集上提出的方法的出色性能，其胭脂l为0.384 $ \ pm $ 0.007，苹果酒为0.340 $ \ pm $ 0.011。与以前的工作相比，我们的模型平均提高了1.6％（苹果酒和胭脂-L分别提高了2.0％和1.5％）。实验表明，先验知识可以为准确的放射学报告生成带来绩效提高。我们将在https://github.com/bionlplab/report_generation_amia2022上公开提供代码。
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.
