Wang Xiaoqian, Liu Kefei, Yan Jingwen, Risacher Shannon L, Saykin Andrew J, Shen Li, Huang Heng
Computer Science & Engineering, University of Texas at Arlington, TX, 76019, USA.
Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Inf Process Med Imaging. 2017 Jun;10265:198-209. doi: 10.1007/978-3-319-59050-9_16. Epub 2017 May 23.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion. In this paper, we proposed a novel self-learned low-rank structured learning model to automatically uncover the interrelations among different classes and utilized such interrelated structures to enhance classification. We conducted experiments on the ADNI cohort data. Empirical results demonstrated advantages of our model.
阿尔茨海默病（AD）是一种进行性神经退行性疾病。作为AD的前驱阶段，轻度认知障碍（MCI）发展为AD的可能性很大。如何有效地检测从MCI到AD的这种转变在AD诊断中具有重要意义。与类别分布相互独立的标准分类问题不同，AD的结果通常是相互关联的（它们的分布有一定的重叠）。现有的大多数方法都未能检验不同类别之间的相互关系，如AD、MCI转变和MCI未转变。在本文中，我们提出了一种新颖的自学习低秩结构化学习模型，以自动揭示不同类别之间的相互关系，并利用这种相互关联的结构来增强分类。我们在ADNI队列数据上进行了实验。实证结果证明了我们模型的优势。
