Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia.
Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.
Comput Med Imaging Graph. 2017 Sep;60:35-41. doi: 10.1016/j.compmedimag.2017.01.001. Epub 2017 Feb 7.
Alzheimer's disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitive impairment (MCI) is considered to be a prodromal stage of AD. The ability to identify subjects with MCI will be increasingly important as disease modifying therapies for AD are developed. We propose a semi-supervised learning method based on robust optimization for the identification of MCI from [18F]Fluorodeoxyglucose PET scans. We extracted three groups of spatial features from the cortical and subcortical regions of each FDG-PET image volume. We measured the statistical uncertainty related to these spatial features via transformation using an incomplete random forest and formulated the MCI identification problem under a robust optimization framework. We compared our approach to other state-of-the-art methods in different learning schemas. Our method outperformed the other techniques in the ability to separate MCI from normal controls.
阿尔茨海默病（AD）是最常见的痴呆症类型，随着人口老龄化，它将成为社会日益严重的健康问题。轻度认知障碍（MCI）被认为是 AD 的前驱阶段。随着 AD 的治疗方法的发展，识别 MCI 患者的能力将变得越来越重要。我们提出了一种基于稳健优化的半监督学习方法，用于从 [18F] 氟脱氧葡萄糖 PET 扫描中识别 MCI。我们从每个 FDG-PET 图像体积的皮质和皮质下区域提取了三组空间特征。我们通过使用不完全随机森林进行变换来测量这些空间特征的统计不确定性，并在稳健优化框架下制定了 MCI 识别问题。我们在不同的学习方案中比较了我们的方法与其他最先进的方法。我们的方法在将 MCI 与正常对照组区分开来的能力方面优于其他技术。
