ArticleIEEE transactions on medical imaging2019
Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 217 citations in OpenAlex.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 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
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation.Scientific reports · 2026Article
- Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.Brain informatics · 2026Review
- An attention-augmented multimodal classification of alzheimer's disease and parkinson's disease vs healthy controls using MRI, EEG, and SNP data.Scientific reports · 2026Article
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease.Journal of neural engineering · 2025Article
- An FDG-PET-Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders.Neurology · 2025Article
- CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.BMC geriatrics · 2025Article
- A Novel Hybrid Ordinal Learning Model with Health Care Application.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Alzheimer's disease recognition via long-range state space model using multi-modal brain images.Frontiers in neuroscience · 2025Article
- BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.IEEE journal of biomedical and health informatics · 2025Article
- Longitudinal Alzheimer's Disease Progression Prediction With Modality Uncertainty and Optimization of Information Flow.IEEE journal of biomedical and health informatics · 2025Article
- Domain adaptation in small-scale and heterogeneous biological datasets.Science advances · 2024Review
- [An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Latent relation shared learning for endometrial cancer diagnosis with incomplete multi-modality medical images.iScience · 2024Article
- Comparing multi-class classifier performance by multi-class ROC analysis: A nonparametric approach.Neurocomputing · 2024Article
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- Credit and Loan Approval Classification Using a Bio-Inspired Neural Network.Biomimetics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The fusion of complementary information contained in multi-modality data [e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and genetic data] has advanced the progress of automated Alzheimer's disease (AD) diagnosis. However, multi-modality based AD diagnostic models are often hindered by the missing data, i.e., not all the subjects have complete multi-modality data. One simple solution used by many previous studies is to discard samples with missing modalities. However, this significantly reduces the number of training samples, thus leading to a sub-optimal classification model. Furthermore, when building the classification model, most existing methods simply concatenate features from different modalities into a single feature vector without considering their underlying associations. As features from different modalities are often closely related (e.g., MRI and PET features are extracted from the same brain region), utilizing their inter-modality associations may improve the robustness of the diagnostic model. To this end, we propose a novel latent representation learning method for multi-modality based AD diagnosis. Specifically, we use all the available samples (including samples with incomplete modality data) to learn a latent representation space. Within this space, we not only use samples with complete multi-modality data to learn a common latent representation, but also use samples with incomplete multi-modality data to learn independent modality-specific latent representations. We then project the latent representations to the label space for AD diagnosis. We perform experiments using 737 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the experimental results verify the effectiveness of our proposed method.
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Registered trials
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 Socratic graph.