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A Dual-View Approach to Classifying Radiology Reports by Co-Training

2024-06-10 03:29:23
Yutong Han, Yan Yuan, Lili Mou


Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In this work, we present a novel insight that the structure of a radiology report (namely, the Findings and Impression sections) offers different views of a radiology scan. Based on this intuition, we further propose a co-training approach, where two machine learning models are built upon the Findings and Impression sections, respectively, and use each other's information to boost performance with massive unlabeled data in a semi-supervised manner. We conducted experiments in a public health surveillance study, and results show that our co-training approach is able to improve performance using the dual views and surpass competing supervised and semi-supervised methods.

Abstract (translated)

放射学报告分析提供了对公共卫生倡议的有价值的资料,并吸引了越来越多的研究人员的关注。在这项工作中,我们提出了一个新见解,即放射学报告的结构(具体而言,发现和印象部分)提供了对放射学扫描的不同看法。基于这一直觉,我们进一步提出了合作训练方法,其中两个机器学习模型分别基于发现和印象部分构建,并使用彼此的信息以半监督方式处理大规模无标签数据,提高性能。我们在公共卫生 surveillance 研究中进行了实验,结果表明,我们的合作训练方法能够使用双视图超越竞争监督和半监督方法。



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