Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis
Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI)
diagnosis has attracted researchers in the field, due to the increasing prevalence of the …
diagnosis has attracted researchers in the field, due to the increasing prevalence of the …
Deep ensemble learning of sparse regression models for brain disease diagnosis
Recent studies on brain imaging analysis witnessed the core roles of machine learning
techniques in computer-assisted intervention for brain disease diagnosis. Of various …
techniques in computer-assisted intervention for brain disease diagnosis. Of various …
Deep ensemble sparse regression network for Alzheimer's disease diagnosis
For neuroimaging-based brain disease diagnosis, sparse regression models have proved
their effectiveness in handling high-dimensional data but with a small number of samples. In …
their effectiveness in handling high-dimensional data but with a small number of samples. In …
Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis
Neurodegenerative diseases are excessively affecting millions of patients, especially elderly
people. Early detection and management of these diseases are crucial as the clinical …
people. Early detection and management of these diseases are crucial as the clinical …
Early diagnosis model of Alzheimer's disease based on sparse logistic regression
R Xiao, X Cui, H Qiao, X Zheng, Y Zhang - Multimedia tools and …, 2021 - Springer
Accurate classification of Alzheimer's Disease (AD) and its prodromal stage, ie, mild
cognitive impairment (MCI) are critical for the effective treatment of AD. However, compared …
cognitive impairment (MCI) are critical for the effective treatment of AD. However, compared …
Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation-and nonlinearity-aware sparse Bayesian learning
Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI)
measures and identifying relevant imaging biomarkers are important research topics in the …
measures and identifying relevant imaging biomarkers are important research topics in the …
A novel relational regularization feature selection method for joint regression and classification in AD diagnosis
In this paper, we focus on joint regression and classification for Alzheimer's disease
diagnosis and propose a new feature selection method by embedding the relational …
diagnosis and propose a new feature selection method by embedding the relational …
Relational-regularized discriminative sparse learning for Alzheimer's disease diagnosis
Accurate identification and understanding informative feature is important for early
Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel …
Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel …
Iterative sparse and deep learning for accurate diagnosis of Alzheimer's disease
Deep learning techniques have been increasingly applied to the diagnosis of Alzheimer's
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …
Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis
Fusing information from different imaging modalities is crucial for more accurate
identification of the brain state because imaging data of different modalities can provide …
identification of the brain state because imaging data of different modalities can provide …