ArticleMedical image analysis2017
A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.
Article in Medical image analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.IEEE transactions on bio-medical engineering · 2022Article
- Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.IEEE transactions on neural networks and learning systems · 2022Article
- A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease.Brain informatics · 2022Article
- Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment.Brain imaging and behavior · 2022Article
- Multi-auxiliary domain transfer learning for diagnosis of MCI conversion.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022Article
- Article
- On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.Journal of healthcare engineering · 2022Article
- Multiscale structural mapping of Alzheimer's disease neurodegeneration.NeuroImage. Clinical · 2022Article
- PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification.Journal of Alzheimer's disease : JAD · 2021Article
- Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion.Computational and mathematical methods in medicine · 2021Article
- Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan.Medical image analysis · 2021Article
- Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer's Disease.Brain sciences · 2020Article
- Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm.Journal of neuroscience methods · 2020Article
- A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs.Journal of neurology · 2020Article
- Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases.Human brain mapping · 2020Article
- Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments.The European journal of neuroscience · 2020Article
- Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.IEEE transactions on pattern analysis and machine intelligence · 2020Article
- A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms.IEEE transactions on bio-medical engineering · 2020Article
- A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.NeuroImage · 2020Article
- 3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.Neuroinformatics · 2020Article
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6 authors.
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Abstract
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 information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ
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