The impression section of a radiology report summarizes the most prominent observation from the findings section and is the most important section for radiologists to communicate to physicians. Summarizing findings is time-consuming and can be prone to error for inexperienced radiologists, and thus automatic impression generation has attracted substantial attention. With the encoder-decoder framework, most previous studies explore incorporating extra knowledge (e.g., static pre-defined clinical ontologies or extra background information). Yet, they encode such knowledge by a separate encoder to treat it as an extra input to their models, which is limited in leveraging their relations with the original findings. To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i.e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation. In detail, for each input findings, it is encoded by a text encoder, and a graph is constructed through its entities and dependency tree. Then, a graph encoder (e.g., graph neural networks (GNNs)) is adopted to model relation information in the constructed graph. Finally, to emphasize the key words in the findings, contrastive learning is introduced to map positive samples (constructed by masking non-key words) closer and push apart negative ones (constructed by masking key words). The experimental results on OpenI and MIMIC-CXR confirm the effectiveness of our proposed method.
翻译:放射学报告的印象部分总结了结论部分最突出的观察,是放射学家与医生沟通的最重要部分。 总结调查结果耗时费时,容易发生对经验不足的放射学家的错误,因此自动生成印象引起了大量关注。 由于编码器解码器框架,大多数先前的研究都探索将额外知识(例如静态预定义临床肿瘤或额外的背景资料)纳入其中。然而,他们用一个单独的编码器将这种知识编码成一个额外的模型输入,这限制了他们与原始发现的关系。为了解决限制问题,我们提出了一个统一框架,以便利用额外知识和原始发现,从而能够以适当的方式提取关键信息(即关键词及其关系),以便于生成印象。对于每一项输入结果,都用一个负面的编码器进行编码,并通过其实体和依赖树制作一个图表,然后用图表编码器(例如,纸质网络的图表,非神经网络的图表)来利用额外的知识和原始发现,以便以更精确的方式提取关键信息(GNNF),通过图表的缩略图的缩略图和缩略图(GNNC),通过图表的缩略图的缩略图与正的缩缩取方法,采用。