ArticleIEEE transactions on bio-medical engineering2015
Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease.
Article in IEEE transactions on bio-medical engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers, 3 of them syntheses that pooled it.
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Who cites it
158 citing papers in PubMed, 3 syntheses or guidelines pooled it, 608 citations in OpenAlex.
- 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
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data.Frontiers in aging neuroscience · 2019Pooled it
- Interpretable multimodal MRI fusion in Alzheimer's disease classification using extended parallel multilink joint ICA and 3D ResNet.iScience · 2026Article
- A comparative study of vision transformer architectures for the detection of Alzheimer's disease using magnetic resonance images.Physical and engineering sciences in medicine · 2026Article
- Topological Data Analysis-Driven fNIRS Signal Processing for Alzheimer's Disease Stage Identification.Sensors (Basel, Switzerland) · 2026Article
- Ricci Flow-Based Approach for Early Diagnosis of Alzheimer's Disease.Neuroinformatics · 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
- Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task.Brain sciences · 2026Article
- AI-enabled digital phenotyping for Alzheimer's disease: a review of multimodal sensor integration and symptom trajectories.Alzheimer's research & therapy · 2026Review
- The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).Neuroinformatics · 2026Review
- Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.Brain informatics · 2026Review
- The best from both disciplines: integrating human and microbial signatures from whole genome sequencing to advance cancer diagnostics.mSystems · 2026Review
- Generalizable structure-function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion.Nature. Mental health · 2026Article
- Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.Scientific reports · 2025Article
- Artificial intelligence in interventional cardiology: a review of its role in diagnosis, decision-making, and procedural precision.Annals of medicine and surgery (2012) · 2025Review
- A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.Scientific reports · 2025Article
- Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.Bioengineering (Basel, Switzerland) · 2025Review
- SAFFusion: a saliency-aware frequency fusion network for multimodal medical image fusion.Biomedical optics express · 2025Article
98 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 3 countries.
Funding
Abstract
The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.
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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 OpenQuestion graph.