[HTML][HTML] 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 …
Deep learning for Alzheimer's disease diagnosis: A survey
M Khojaste-Sarakhsi, SS Haghighi… - Artificial intelligence in …, 2022 - Elsevier
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
[HTML][HTML] Multimodal deep learning models for early detection of Alzheimer's disease stage
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
[HTML][HTML] A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease
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 …
cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can …
Self-supervision with superpixels: Training few-shot medical image segmentation without annotation
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …
Most of the existing FSS techniques require abundant annotated semantic classes for …
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 …
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 …
Cognitive decline in Parkinson disease
Dementia is a frequent problem encountered in advanced stages of Parkinson disease (PD).
In recent years, research has focused on the pre-dementia stages of cognitive impairment in …
In recent years, research has focused on the pre-dementia stages of cognitive impairment in …
[PDF][PDF] Dynamic hypergraph neural networks.
In recent years, graph/hypergraph-based deep learning methods have attracted much
attention from researchers. These deep learning methods take graph/hypergraph structure …
attention from researchers. These deep learning methods take graph/hypergraph structure …
Preclinical Alzheimer's disease: definition, natural history, and diagnostic criteria
During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD)
considering the disease as a continuum. Thanks to evolving biomarker research and …
considering the disease as a continuum. Thanks to evolving biomarker research and …