AIMC Topic: Alzheimer Disease
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A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Sep 13, 2018
Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been considered as promising biomarkers for early Alzheimer's disease (AD) diagnosis, it is still challenging to...
Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis.
IEEE transactions on bio-medical engineering
Sep 12, 2018
In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlate...
Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.
Scientific reports
Sep 5, 2018
Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal...
Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data.
NeuroImage
Aug 18, 2018
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they ...
Radiopharmaceuticals
Alzheimer Disease
Positron-Emission Tomography
Datasets as Topic
Humans
Middle Aged
Aged
Fluorodeoxyglucose F18
Machine Learning
Image Processing, Computer-Assisted
Aged, 80 and over
Neuroimaging
Female
Male
Magnetic Resonance Imaging
Atlases as Topic
Data Interpretation, Statistical
Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases.
NeuroImage
Aug 18, 2018
INTRODUCTION: The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) usi...
Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis.
CNS neuroscience & therapeutics
Aug 14, 2018
AIMS: Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune-relevant AD drug target genes have been discovered up to ...
Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease.
IEEE journal of biomedical and health informatics
Jul 18, 2018
Using a single imaging modality to diagnose Alzheimer's disease (AD) or mild cognitive impairment (MCI) is a challenging task. FluoroDeoxyGlucose Positron Emission Tomography (FDG-PET) is an important and effective modality used for that purpose. In ...
GOATOOLS: A Python library for Gene Ontology analyses.
Scientific reports
Jul 18, 2018
The biological interpretation of gene lists with interesting shared properties, such as up- or down-regulation in a particular experiment, is typically accomplished using gene ontology enrichment analysis tools. Given a list of genes, a gene ontology...
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.
Medical image analysis
Jun 2, 2018
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patien...
Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification.
NeuroImage
May 23, 2018
In recent years, machine learning approaches have been successfully applied to the field of neuroimaging for classification and regression tasks. However, many approaches do not give an intuitive relation between the raw features and the diagnosis. T...
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