An Le, Adeli Ehsan, Liu Mingxia, Zhang Jun, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:79-87. doi: 10.1007/978-3-319-46723-8_10. Epub 2016 Oct 2.
Alzheimer's disease (AD) is a progressive neurodegenerative disease that impairs a patient's memory and other important mental functions. In this paper, we leverage the mutually informative and complementary features from both structural magnetic resonance imaging (MRI) and single nucleotide polymorphism (SNP) for improving the diagnosis. Due to the feature redundancy and sample outliers, direct use of all training data may lead to suboptimal performance in classification. In addition, as redundant features are involved, the most discriminative feature subset may not be identified in a single step, as commonly done in most existing feature selection approaches. Therefore, we formulate a hierarchical multimodal feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps. To positively guide the data manifold preservation, we utilize both labeled and unlabeled data in the learning process, making our method semi-supervised. The finally selected features and samples are then used to train support vector machine (SVM) based classification models. Our method is evaluated on 702 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and the superior classification results in AD related diagnosis demonstrate the effectiveness of our approach as compared to other methods.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，会损害患者的记忆和其他重要的心理功能。在本文中，我们利用来自结构磁共振成像（MRI）和单核苷酸多态性（SNP）的相互信息和互补特征来改善诊断。由于特征冗余和样本离群值，直接使用所有训练数据可能会导致分类性能次优。此外，由于涉及冗余特征，最具判别力的特征子集可能无法像大多数现有特征选择方法那样在单个步骤中被识别出来。因此，我们制定了一个分层多模态特征和样本选择框架，以逐步在多个步骤中选择信息性特征并丢弃模糊样本。为了积极引导数据流形的保留，我们在学习过程中同时使用标记数据和未标记数据，使我们的方法具有半监督性。然后，最终选择的特征和样本用于训练基于支持向量机（SVM）的分类模型。我们的方法在来自阿尔茨海默病神经影像学倡议（ADNI）数据集的702名受试者上进行了评估，与其他方法相比，在AD相关诊断中优越的分类结果证明了我们方法的有效性。
