ArticleHuman brain mapping2019
Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
81 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal Deep Learning Approaches for Early Detection of Alzheimer's Disease: A Comprehensive Systematic Review of Image Processing Techniques.Current Alzheimer research · 2025Pooled it
- ACPC-MAP: a protocol for manually aligning structural T1-weighted magnetic resonance images to the anterior commissure - posterior commissure plane according to clinical standards.Brain structure & function · 2026Article
- FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.Diagnostics (Basel, Switzerland) · 2026Article
- Vision transformer autoencoders captures local and non-local features in brain imaging to reveal novel genetic associations.Communications biology · 2026Article
- Functional-Structural Coupling: Brain Reorganization in Presbycusis Is Related to Cognitive Impairment.eNeuro · 2026Article
- Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction.Applications of medical artificial intelligence. AMAI (Workshop) (4th : 2024 : Taejon-si, Korea) · 2026Article
- Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- Neuroengineering Frontiers: A Selective Review of Neural Interfaces, Brain-Machine Interactions, and Artificial Intelligence in Neurodegenerative Diseases.Applied sciences (Basel, Switzerland) · 2025Article
- CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.BMC geriatrics · 2025Article
- Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.Scientific reports · 2025Article
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 2025Article
- Caregiver perspectives enable accurate diagnosis of neurodegenerative disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.PloS one · 2025Article
- An imaging and genetic-based deep learning network for Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2025Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Hydra-TS: Enhancing Human Activity Recognition with Multi-Objective Synthetic Time Series Data Generation.IEEE sensors journal · 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
- The top 100 cited articles on artificial intelligence in Alzheimer's disease and mild cognitive impairment: a bibliometric analysis.Frontiers in aging neuroscience · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimaging data such as MRI and PET provide valuable insights into brain abnormalities, while genetic data such as single nucleotide polymorphism (SNP) provide information about a patient's AD risk factors. When these data are used together, the accuracy of AD diagnosis may be improved. However, these data are heterogeneous (e.g., with different data distributions), and have different number of samples (e.g., with far less number of PET samples than the number of MRI or SNPs). Thus, learning an effective model using these data is challenging. To this end, we present a novel three-stage deep feature learning and fusion framework, where deep neural network is trained stage-wise. Each stage of the network learns feature representations for different combinations of modalities, via effective training using the maximum number of available samples. Specifically, in the first stage, we learn latent representations (i.e., high-level features) for each modality independently, so that the heterogeneity among modalities can be partially addressed, and high-level features from different modalities can be combined in the next stage. In the second stage, we learn joint latent features for each pair of modality combination by using the high-level features learned from the first stage. In the third stage, we learn the diagnostic labels by fusing the learned joint latent features from the second stage. To further increase the number of samples during training, we also use data at multiple scanning time points for each training subject in the dataset. We evaluate the proposed framework using Alzheimer's disease neuroimaging initiative (ADNI) dataset for AD diagnosis, and the experimental results show that the proposed framework outperforms other state-of-the-art methods.
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What Socratic holds
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.