ArticleBrain imaging and behavior2019
Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease.
Article in Brain imaging and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer's disease.Frontiers in genetics · 2025Article
- A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease.Biomolecules · 2023Article
- Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives.Neuroinformatics · 2023Review
- Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion.Human brain mapping · 2022Article
- Fast Image-Level MRI Harmonization via Spectrum Analysis.Machine learning in medical imaging. MLMI (Workshop) · 2022Article
- A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- 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
- Differential Role for Hippocampal Subfields in Alzheimer's Disease Progression Revealed with Deep Learning.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images.Alzheimer's research & therapy · 2022Article
- Diagnostic model optimization method for ADHD based on brain network analysis of resting-state fMRI images and transfer learning neural network.Frontiers in human neuroscience · 2022Article
- Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.Current genomics · 2021Review
- Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review.Journal of imaging · 2021Review
- Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.Neuroinformatics · 2020Article
- Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks.Frontiers in neuroscience · 2020Article
- Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort.Bioinformatics (Oxford, England) · 2019Article
- A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes.Frontiers in neuroscience · 2018Article
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Authors and funding
5 authors.
Funding
Abstract
Transfer learning has been successfully used in the early diagnosis of Alzheimer's disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.