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
this https URL.

本文提出了一种基于知识图谱的深度学习方法，将医学发现之间的相互关系挖掘和表示，并将这些先验知识用于辅助放射学报告生成，实验结果表明，相较于过去的方法，该模型性能有显著提升。

先验知识增强放射学报告生成

Prior Knowledge Enhances Radiology Report Generation

Negative medical findings are prevalent in clinical reports, yet
discriminating them from positive findings remains a challenging task for
information extraction. Most of the existing systems treat this task as a
pipeline of two separate tasks, i.e., named entity recognition (NER) and
rule-based negation detection. We consider this as a multi-task problem and
present a novel end-to-end neural model to jointly extract entities and
negations. We extend a standard hierarchical encoder-decoder NER model and
first adopt a shared encoder followed by separate decoders for the two tasks.
This architecture performs considerably better than the previous rule-based and
machine learning-based systems. To overcome the problem of increased parameter
size especially for low-resource settings, we propose the Conditional Softmax
Shared Decoder architecture which achieves state-of-art results for NER and
negation detection on the 2010 i2b2/VA challenge dataset and a proprietary
de-identified clinical dataset.

提出一种基于神经网络并采用多任务学习方法的信息提取模型，用于同时提取医学实体和否定词，该模型在低资源情况下使用基于条件 softmax 的共享解码器，实现了在 2010i2b2/VA 挑战数据集和专有的医学数据集上最先进的 NER 和否定检测效果。