A Dual-View Approach to Classifying Radiology Reports by Co-Training
This work addresses the challenge of analyzing radiology reports for public health initiatives, offering an incremental improvement through a novel co-training method.
The paper tackled the problem of classifying radiology reports by leveraging the dual views of Findings and Impression sections, proposing a co-training approach that improved performance using unlabeled data and surpassed competing methods in a public health surveillance study.
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.