Random forest-based similarity measures for multi-modal classification of Alzheimer's disease.
Random forest-based similarity measures for multi-modal classification of Alzheimer's disease.
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DOI：
10.1016/j.neuroimage.2012.09.065
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发表时间：
2013-01-15
期刊：
影响因子：
5.7
通讯作者：
Rueckert, Daniel
中科院分区：
文献类型：
--
作者：
Gray, Katherine R.;Aljabar, Paul;Heckemann, Rolf A.;Hammers, Alexander;Rueckert, Daniel
关键词：
神经退行性疾病，如阿尔茨海默病，与多种神经成像和生物学指标的变化有关。这些可能为诊断和预后提供补充信息。我们提出了一种多通道分类框架，其中流形是基于来自随机森林分类器的成对相似性度量来构建的。来自多个模态的相似性被组合以生成同时编码关于所有可用特征的信息的嵌入。然后使用来自该联合嵌入的坐标来执行多通道分类。我们通过对阿尔茨海默病神经成像倡议(ADNI)的神经成像和生物学数据的应用来评估所提出的框架。特征包括区域MRI体积、基于体素的FDG-PET信号强度、脑脊液生物标记物测量和分类遗传信息。对于阿尔茨海默病患者和健康对照组之间以及轻度认知障碍患者和健康对照组之间的比较，基于使用所有四种模式的信息构建的联合嵌入的分类效果优于基于任何单个模式的分类。基于联合嵌入，我们在阿尔茨海默病患者和健康对照组之间达到了89%的分类准确率，在轻度认知障碍患者和健康对照组之间达到了75%的分类准确率。这些结果与最近使用多核学习的其他研究报告的结果相当。随机森林为多个模式提供了一致的成对相似性度量，从而促进了不同类型特征数据的组合。我们通过对数据的应用来证明这一点，在这些数据中，不同模式之间的特征数量相差几个数量级。随机森林分类器自然扩展到多类问题，这里描述的框架可以在未来应用于区分多个患者组。
Neurodegenerative disorders, such as Alzheimer’s disease, are associated with changes in multiple neuroimaging and biological measures. These may provide complementary information for diagnosis and prognosis. We present a multi-modality classification framework in which manifolds are constructed based on pairwise similarity measures derived from random forest classifiers. Similarities from multiple modalities are combined to generate an embedding that simultaneously encodes information about all the available features. Multimodality classification is then performed using coordinates from this joint embedding. We evaluate the proposed framework by application to neuroimaging and biological data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Features include regional MRI volumes, voxel-based FDG-PET signal intensities, CSF biomarker measures, and categorical genetic information. Classification based on the joint embedding constructed using information from all four modalities out-performs classification based on any individual modality for comparisons between Alzheimer’s disease patients and healthy controls, as well as between mild cognitive impairment patients and healthy controls. Based on the joint embedding, we achieve classification accuracies of 89% between Alzheimer’s disease patients and healthy controls, and 75% between mild cognitive impairment patients and healthy controls. These results are comparable with those reported in other recent studies using multi-kernel learning. Random forests provide consistent pairwise similarity measures for multiple modalities, thus facilitating the combination of different types of feature data. We demonstrate this by application to data in which the number of features differ by several orders of magnitude between modalities. Random forest classifiers extend naturally to multi-class problems, and the framework described here could be applied to distinguish between multiple patient groups in the future.
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影响因子：
--
作者：
Anchisi, D;Borroni, B;Perani, D
通讯作者：
Perani, D
影响因子：
5.7
作者：
Gousias, Ioannis S.;Rueckert, Daniel;Hammers, Alexander
通讯作者：
Hammers, Alexander
影响因子：
2.9
作者：
Amit, Y;Geman, D
通讯作者：
Geman, D
DOI：
10.1016/j.jalz.2011.03.008
发表时间：
2011-05
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Albert MS;DeKosky ST;Dickson D;Dubois B;Feldman HH;Fox NC;Gamst A;Holtzman DM;Jagust WJ;Petersen RC;Snyder PJ;Carrillo MC;Thies B;Phelps CH
通讯作者：
Phelps CH
影响因子：
--
作者：
Andreasen, N;Minthon, L;Blennow, K
通讯作者：
Blennow, K