AIMC Topic: Alzheimer Disease

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A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine Jan 22, 2020
PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning ha...
Humans Disease Progression Case-Control Studies Artificial Intelligence Diagnosis, Computer-Assisted Brain Deep Learning Female Alzheimer Disease Aged Reproducibility of Results Pattern Recognition, Automated Aged, 80 and over Neuroimaging Imaging, Three-Dimensional Male Hydrocephalus, Normal Pressure Image Processing, Computer-Assisted Middle Aged Magnetic Resonance Imaging Diagnosis, Differential
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A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease.

Computer methods and programs in biomedicine Jan 20, 2020
BACKGROUND AND OBJECTIVES: Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the dis...
Humans Longitudinal Studies Machine Learning Alzheimer Disease Disease Progression Neuroimaging
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Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease.

Medical image analysis Jan 17, 2020
Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonl...
Humans Machine Learning Alzheimer Disease Early Diagnosis Neuroimaging Calibration
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Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.

NeuroImage. Clinical Jan 17, 2020
Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We arg...
Adult Deep Learning Adolescent Child Humans Autism Spectrum Disorder Aged Young Adult Nerve Net Magnetic Resonance Imaging Alzheimer Disease Cerebral Cortex Connectome
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Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis.

Medical image analysis Jan 8, 2020
Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases are crucial as the clinical symptoms take years to appear after the onset of neuro-degeneration. Th...
Image Interpretation, Computer-Assisted Datasets as Topic Early Diagnosis Neuroimaging Machine Learning Parkinson Disease Neurodegenerative Diseases Alzheimer Disease Humans
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A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Computational and mathematical methods in medicine Dec 30, 2019
Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegene...
Image Interpretation, Computer-Assisted Reproducibility of Results Linear Models Aged Databases, Factual Disease Progression Humans Pattern Recognition, Automated Male Biomarkers Diagnosis, Computer-Assisted Machine Learning Neuroimaging Middle Aged Cognitive Dysfunction Brain Algorithms Magnetic Resonance Imaging Image Processing, Computer-Assisted Female Discriminant Analysis Alzheimer Disease Positron-Emission Tomography
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Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.

International journal of computer assisted radiology and surgery Dec 27, 2019
PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensitive lesions can provide a visual basis for the diagnosis results. This study proposes an improved end...
Magnetic Resonance Imaging Alzheimer Disease Hippocampus Humans Neural Networks, Computer Image Processing, Computer-Assisted
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Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Dec 19, 2019
Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the multi-modality based methods usuall...
Neuroimaging Alzheimer Disease Image Interpretation, Computer-Assisted Pattern Recognition, Automated Aged, 80 and over Male Humans Brain Mapping Machine Learning Multimodal Imaging Female Aged
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A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

NeuroImage Dec 16, 2019
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its progression, no effective cures are available for AD. Accura...
Neuroimaging Magnetic Resonance Imaging Alzheimer Disease Image Interpretation, Computer-Assisted Hippocampus Male Cognitive Dysfunction Models, Theoretical Humans Neural Networks, Computer Female Aged Aged, 80 and over Sensitivity and Specificity
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Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

NeuroImage Dec 10, 2019
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional meas...
Humans Machine Learning Biomarkers Female Reproducibility of Results Atrophy Magnetic Resonance Imaging Executive Function Alzheimer Disease Aged Frontotemporal Dementia Aged, 80 and over Neuropsychological Tests Male Middle Aged
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