A Dual-View Approach to Classifying Radiology Reports by Co-Training
International Conference on Computational Linguistics(2024)
摘要
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.
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