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Random forest-based similarity measures for multi-modal classification of Alzheimer's disease
2013
NeuroImage
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
doi:10.1016/j.neuroimage.2012.09.065
pmid:23041336
pmcid:PMC3516432
fatcat:ysrja6zfc5d35gr5dav42dwkl4