  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.
