Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …
remarkable performance in providing medical professionals and patients with support for …
Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Explainable tensor multi-task ensemble learning based on brain structure variation for Alzheimer's Disease dynamic prediction
Machine learning approaches for predicting Alzheimer's disease (AD) progression can
substantially assist researchers and clinicians in developing effective AD preventive and …
substantially assist researchers and clinicians in developing effective AD preventive and …
A multi-modal data platform for diagnosis and prediction of Alzheimer's disease using machine learning methods
Alzheimer's an irreversible neurodegenerative disease with the most far-reaching impact,
the most extensive, and the most difficult to cure in the world. It is also the most common …
the most extensive, and the most difficult to cure in the world. It is also the most common …
Dual feature correlation guided multi-task learning for Alzheimer's disease prediction
Alzheimer's disease (AD) is a gradually progressive neurodegenerative disease affecting
cognition functions. Predicting the cognitive scores from neuroimage measures and …
cognition functions. Predicting the cognitive scores from neuroimage measures and …
A survey of disease progression modeling techniques for alzheimer's diseases
Modeling and predicting progression of chronic diseases like Alzheimer's disease (AD) has
recently received much attention. Traditional approaches in this field mostly rely on …
recently received much attention. Traditional approaches in this field mostly rely on …
Tensor multi-task learning for predicting alzheimer's disease progression using MRI data with spatio-temporal similarity measurement
Alzheimer's disease (AD) is a typical progressive neurodegenerative disease with insidious
onset. Utilising various biomarkers to track and predict AD progression for supporting clinic …
onset. Utilising various biomarkers to track and predict AD progression for supporting clinic …
Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease
Objective Alzheimers disease (AD) is characterized by gradual neurodegeneration and loss
of brain function, especially for memory during early stages. Regression analysis has been …
of brain function, especially for memory during early stages. Regression analysis has been …
A novel approach of diffusion tensor visualization based neuro fuzzy classification system for early detection of Alzheimer's disease
S Kar, DD Majumder - Journal of Alzheimer's disease reports, 2019 - content.iospress.com
This study examined early detection of Alzheimer's disease (AD) by diffusion tensor
visualization-based methodology and neuro-fuzzy tools. Initially, we proposed a model for …
visualization-based methodology and neuro-fuzzy tools. Initially, we proposed a model for …
[HTML][HTML] Exploring automated machine learning for cognitive outcome prediction from multimodal brain imaging using streamline
STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML)
pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The …
pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The …