半监督模式分类：在阿尔茨海默病结构磁共振成像中的应用
Semi-Supervised Pattern Classification: Application to Structural MRI of Alzheimer's Disease.
作者信息
Ye Dong Hye, Pohl Kilian M, Davatzikos Christos
机构信息
Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, United States 19104.
出版信息
Int Workshop Pattern Recognit Neuroimaging. 2011 May;2011:1-4. doi: 10.1109/PRNI.2011.12. Epub 2011 Jul 25.
This paper presents an image-based classification method, and applies it to classification of brain MRI scans of individuals with Mild Cognitive Impairment (MCI). The high dimensionality of the image data is reduced using nonlinear manifold learning techniques, thereby yielding a low-dimensional embedding. Features of the embedding are used in conjunction with a semi-supervised classifier, which utilizes both labeled and unlabeled images to boost performance. The method is applied to 237 scans of MCI patients in order to predict conversion from MCI to Alzheimer's Disease. Experimental results demonstrate better prediction accuracy compared to a state-of-the-art method.
本文提出了一种基于图像的分类方法，并将其应用于轻度认知障碍（MCI）个体的脑部磁共振成像（MRI）扫描分类。使用非线性流形学习技术降低图像数据的高维性，从而得到低维嵌入。嵌入的特征与半监督分类器结合使用，该分类器利用标记和未标记的图像来提高性能。该方法应用于237例MCI患者的扫描，以预测MCI向阿尔茨海默病的转化。实验结果表明，与一种先进方法相比，该方法具有更高的预测准确率。
Zotero 插件