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

Autor:	Han, Yutong, Yuan, Yan, Mou, Lili
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by LREC-COLING 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.05995 Zobrazit plný text záznamu View this record from Arxiv