Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures
Alzheimer's disease (AD) has been not only a substantial financial burden to the health care
system but also an emotional burden to patients and their families. Making accurate …
system but also an emotional burden to patients and their families. Making accurate …
Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification
In this paper, we propose a novel dimensionality reduction method of taking the advantages
of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer's Disease …
of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer's Disease …
Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, eg, mild cognitive
impairment (MCI), is essential for timely treatment or possible intervention to slow down AD …
impairment (MCI), is essential for timely treatment or possible intervention to slow down AD …
Nonlinear dimensionality reduction combining MR imaging with non-imaging information
We propose a framework for the extraction of biomarkers from low-dimensional manifolds
representing inter-subject brain variation. Manifold coordinates of each image capture …
representing inter-subject brain variation. Manifold coordinates of each image capture …
Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm
Background Using multiple modalities of biomarkers, several machine leaning-based
approaches have been proposed to characterize patterns of structural, functional and …
approaches have been proposed to characterize patterns of structural, functional and …
Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis
In this study, we develop a novel nonlinear metric learning method to improve biomarker
identification for Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). Formulated …
identification for Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). Formulated …
Deep ordinal ranking for multi-category diagnosis of Alzheimer's disease using hippocampal MRI data
Increasing effort in brain image analysis has been dedicated to early diagnosis of
Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been …
Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been …
High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization
R Casanova, CT Whitlow, B Wagner… - Frontiers in …, 2011 - frontiersin.org
In this work we use a large scale regularization approach based on penalized logistic
regression to automatically classify structural MRI images (sMRI) according to cognitive …
regression to automatically classify structural MRI images (sMRI) according to cognitive …
Semi-supervised hierarchical multimodal feature and sample selection for Alzheimer's disease diagnosis
Alzheimer's disease (AD) is a progressive neurodegenerative disease that impairs a
patient's memory and other important mental functions. In this paper, we leverage the …
patient's memory and other important mental functions. In this paper, we leverage the …
Sparse feature learning with label information for Alzheimer's disease classification based on magnetic resonance imaging
Neuroimaging techniques have been used for automatic diagnosis and classification of
Alzheimer's disease and mild cognitive impairment. How to select discriminant features from …
Alzheimer's disease and mild cognitive impairment. How to select discriminant features from …