用于阿尔茨海默病诊断的权值分布特征学习

Feature Learning of Weight-distribution for Diagnosis of Alzheimer's Disease

针对当前基于机器学习的早期阿尔茨海默病(AD)诊断中有标记训练样本不足的问题,提出一种基于多模态特征数据的权值分布稀疏特征学习方法,并将其应用于早期阿尔茨海默病的诊断.具体来说,该诊断方法主要包括两大模块:基于权值分布的Lasso特征选择模型(WDL)和大间隔分布分类机模型(LDM).首先,为了获取多模态特征之间的数据分布信息,对传统Lasso模型进行改进,引入权值分布正则化项,从而构建出基于权值分布的Lasso特征选择模型;然后,为了有效地利用多模态特征之间的数据分布信息,以保持多模态特征之间的互补性,直接采用大间隔分布学习算法训练分类器.选取国际阿尔茨海默症数据库(ADNI)中202个多模态特征的被试者样本进行实验,分类AD最高平均精度为97.5%,分类轻度认知功能障碍(MCI)最高平均精度为83.1%,分类轻度认知功能障碍转化为AD(p MCI)最高平均精度为84.8%.实验结果表明,所提WDL特征学习方法可从串联的多模态特征学到性能更优的特征子集,并能根据权值分布获取多模态特征之间的数据分布信息,从而提高早期阿尔茨海默病诊断的性能.

In the field of medical imaging analysis using machine learning, the challenge is lack of training sample. In order to solve the problem, a weight-distribution based Lasso (Least absolute shrinkage and selection operator) feature learning model is proposed and applied to early diagnosis of Alzheimer’s Disease (AD). Specifically, the proposed diagnosis method is consisted of two components: weight-distribution based Lasso feature selection (WDL) and large margin distribution machine (LDM) for classification. Firstly, in order to capture data distribution information among multimodal features, the WDL feature selection model was built, to improve on the conventional Lasso model via adding a regularization item of weight-distribution. Secondly, in order to achieve better generalization and accuracy on classification, and also to keep complementary information among multimodal features, the LDM algorithm is used for the training of the classifier. To evaluate the effectiveness of the proposed learning model, 202 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with multimodal features were employed. Experimental results on the ADNI database show that it can recognize AD from Normal Controls (NC) with 97.5% accuracy, recognize Mild Cognitive Impairment (MCI) from NC with 83.1% accuracy, and recognize progressive MCI (pMCI) patients from stable MCI (sMCI) ones with 84.8% accuracy, which demonstrate that it can significantly improve the performance of early AD diagnosis and achieve feature ranking in terms of discrimination via optimized weight vector.