A Dual-View Approach to Classifying Radiology Reports by Co-Training
<|reference_start|>A Dual-View Approach to Classifying Radiology Reports by Co-Training: 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.<|reference_end|>
@article{han2024a,
    title={A Dual-View Approach to Classifying Radiology Reports by Co-Training},
    author={Yutong Han, Yan Yuan, Lili Mou},
    journal={arXiv preprint arXiv:2406.05995},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05995},
    primaryClass={cs.CL cs.AI cs.LG}
}