论文标题
使用功能回归使用胸部X射线对小儿肺炎进行分类
Classification of pediatric pneumonia using chest X-rays by functional regression
论文作者
论文摘要
对小儿肺炎的准确及时诊断对于成功的治疗干预至关重要。一种诊断肺炎病例的方法是使用射线照相数据。在本文中，我们提出了一种新颖的简约标量形象分类模型，该模型采用了功能数据分析的思想。我们的主要思想是将图像视为功能测量，并利用基本的协方差结构来选择基础功能；然后将这些碱基用于近似图像曲线和相应的回归系数。我们将回归模型重新表达为标准的广义线性模型，其中功能主成分得分被视为协变量。我们将该方法应用于（1）针对细菌性肺炎患者的健康和病毒的肺炎，以及（2）测试图像和反应之间关联的无效效应。广泛的仿真研究在分类，假设检验和有效计算方面表明了出色的数值性能。
An accurate and prompt diagnosis of pediatric pneumonia is imperative for successful treatment intervention. One approach to diagnose pneumonia cases is using radiographic data. In this article, we propose a novel parsimonious scalar-on-image classification model adopting the ideas of functional data analysis. Our main idea is to treat images as functional measurements and exploit underlying covariance structures to select basis functions; these bases are then used in approximating both image profiles and corresponding regression coefficient. We re-express the regression model into a standard generalized linear model where the functional principal component scores are treated as covariates. We apply the method to (1) classify pneumonia against healthy and viral against bacterial pneumonia patients, and (2) test the null effect about the association between images and responses. Extensive simulation studies show excellent numerical performance in terms of classification, hypothesis testing, and efficient computation.
