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Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation
and cognitive function impairment in elderly people. The irreversible and devastating …
and cognitive function impairment in elderly people. The irreversible and devastating …
Dual attention multi-instance deep learning for Alzheimer's disease diagnosis with structural MRI
Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological
disease diagnosis, which could reflect the variations of brain. However, due to the local …
disease diagnosis, which could reflect the variations of brain. However, due to the local …
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 …
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Neurodegenerative diseases have shown an increasing incidence in the older population in
recent years. A significant amount of research has been conducted to characterize these …
recent years. A significant amount of research has been conducted to characterize these …
Landmark-based deep multi-instance learning for brain disease diagnosis
Abstract In conventional Magnetic Resonance (MR) image based methods, two stages are
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …
Multi-method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer's disease based on deep learning and hybrid methods
Dementia and Alzheimer's disease are caused by neurodegeneration and poor
communication between neurons in the brain. So far, no effective medications have been …
communication between neurons in the brain. So far, no effective medications have been …
Transformed domain convolutional neural network for Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has become a prevalent and potent imaging
modality for the computer-aided diagnosis (CAD) of neurological diseases like dementia …
modality for the computer-aided diagnosis (CAD) of neurological diseases like dementia …
An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild
cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has …
cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has …
A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity
Brain connectivity alterations associated with mental disorders have been widely reported in
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …