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Multimodal machine learning in precision health: A sco** review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Ensemble deep learning for Alzheimer's disease characterization and estimation
Alzheimer's disease, which is characterized by a continual deterioration of cognitive abilities
in older people, is the most common form of dementia. Neuroimaging data, for example …
in older people, is the most common form of dementia. Neuroimaging data, for example …
A review of deep transfer learning approaches for class-wise prediction of Alzheimer's disease using MRI images
Alzheimer's disease is an irreversible, progressive neurodegenerative disorder that destroys
the brain and memory functionalities. In Alzheimer's disease, the brain starts shrinking, and …
the brain and memory functionalities. In Alzheimer's disease, the brain starts shrinking, and …
[HTML][HTML] A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer's disease
Early detection and treatment can slow the progression of Alzheimer's Disease (AD), one of
the most common neurodegenerative diseases. Recent studies have demonstrated the …
the most common neurodegenerative diseases. Recent studies have demonstrated the …
Automatic analysis of MRI images for early prediction of Alzheimer's disease stages based on hybrid features of CNN and handcrafted features
Alzheimer's disease (AD) is considered one of the challenges facing health care in the
modern century; until now, there has been no effective treatment to cure it, but there are …
modern century; until now, there has been no effective treatment to cure it, but there are …
[HTML][HTML] Advances in multi-target fluorescent probes for imaging and analyzing biomarkers in Alzheimer's disease
C Zhu, J Han, F Liang, M Zhu, G Zhang… - Coordination Chemistry …, 2024 - Elsevier
Alzheimer's disease (AD) is a neurodegenerative condition that poses multiple challenges
for disease diagnosis and treatment. Biomarker-based assays are one of the important tools …
for disease diagnosis and treatment. Biomarker-based assays are one of the important tools …
Regulation of neuroinflammation in Alzheimer's disease via nanoparticle-loaded phytocompounds with anti-inflammatory and autophagy-inducing properties
Background Alzheimer's disease (AD) is characterized by neuroinflammation linked to
amyloid β (Aβ) aggregation and phosphorylated tau (τ) protein in neurofibrillary tangles …
amyloid β (Aβ) aggregation and phosphorylated tau (τ) protein in neurofibrillary tangles …
Radiomics, deep learning and early diagnosis in oncology
P Wei - Emerging topics in life sciences, 2021 - portlandpress.com
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance
imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response …
imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response …
Deep Learning Approaches for Early Prediction of Conversion from MCI to AD using MRI and Clinical Data: A Systematic Review
Due to the absence of definitive treatment for Alzheimer's disease (AD), slowing its
development is essential. Accurately predicting the conversion of mild cognitive impairment …
development is essential. Accurately predicting the conversion of mild cognitive impairment …
[HTML][HTML] Ranking Influential Non-Content Factors on Scientific Papers' Citation Impact: A Multidomain Comparative Analysis
The influence of scientific papers is measured by their citations. Although predicting the
papers' citation impact based on non-content factors has garnered extensive attention, the …
papers' citation impact based on non-content factors has garnered extensive attention, the …