Lu Shen, Xia Yong, Cai Tom Weidong, Feng David Dagan
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2251-4. doi: 10.1109/EMBC.2015.7318840.
Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.
痴呆症，尤其是阿尔茨海默病（AD）是一个全球性问题，对老年人群构成巨大威胁。需要一种基于图像的计算机辅助痴呆症诊断方法，以便在医学图像检查过程中为医生提供帮助。已经提出了许多基于机器学习的利用医学成像的痴呆症分类方法，其中大多数都取得了准确的结果。然而，这些方法大多采用监督学习，需要完全标注的图像数据集，这在实际临床环境中通常并不实用。使用大量未标注图像可以提高痴呆症分类性能。在本研究中，我们提出了一种基于具有亲和正则化的随机流形学习的新型半监督痴呆症分类方法。从正电子发射断层扫描（PET）图像中提取三组空间特征，以构建一个无监督随机森林，然后用于正则化流形学习目标函数。将所提出的方法、最先进的拉普拉斯支持向量机（LapSVM）和监督支持向量机应用于对AD和正常对照（NC）进行分类。实验结果表明，使用未标注图像进行学习确实提高了分类性能。并且我们的方法在同一数据集上优于LapSVM。
