A review of feature selection methods in medical applications
B Remeseiro, V Bolon-Canedo - Computers in biology and medicine, 2019 - Elsevier
Feature selection is a preprocessing technique that identifies the key features of a given
problem. It has traditionally been applied in a wide range of problems that include biological …
problem. It has traditionally been applied in a wide range of problems that include biological …
Feature selection for text classification: A review
Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of
video, audio, text, and images. This is due to the prevalence of novel applications in recent …
video, audio, text, and images. This is due to the prevalence of novel applications in recent …
Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
Hi-net: hybrid-fusion network for multi-modal MR image synthesis
Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer's disease using structural MR and FDG-PET images
Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for
disease based on pathophysiology may be able to provide objective measures for disease …
disease based on pathophysiology may be able to provide objective measures for disease …
Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM
Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
One-step multi-view spectral clustering
Previous multi-view spectral clustering methods are a two-step strategy, which first learns a
fixed common representation (or common affinity matrix) of all the views from original data …
fixed common representation (or common affinity matrix) of all the views from original data …
Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2017 - Elsevier
Abstract Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued
development and standardization of methodologies for biomarkers and has provided an …
development and standardization of methodologies for biomarkers and has provided an …