ArticleMedical image analysis2020
Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 104 citations in OpenAlex.
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
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- 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
- 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
- Domain adaptation in small-scale and heterogeneous biological datasets.Science advances · 2024Review
- Layer-selective deep representation to improve esophageal cancer classification.Medical & biological engineering & computing · 2024Article
- Evolutionary neural architecture search for automated MDD diagnosis using multimodal MRI imaging.iScience · 2024Article
- Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study.iScience · 2024Article
- A radiomics-based brain network in T1 images: construction, attributes, and applications.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Early Prediction of Progression to Alzheimer's Disease using Multi-Modality Neuroimages by a Novel Ordinal Learning Model ADPacer.IISE transactions on healthcare systems engineering · 2024Article
- Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer's Disease Diagnosis: Enhancing Data Integration and Feature Extraction.Journal of personalized medicine · 2023Article
- Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer's Disease Classification.Brain sciences · 2023Article
- Supervised Phenotype Discovery From Multimodal Brain Imaging.IEEE transactions on medical imaging · 2023Article
- Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics.Frontiers in aging neuroscience · 2023Article
- Dopamine Release Impairments Accompany Locomotor and Cognitive Deficiencies in Rotenone-Treated Parkinson's Disease Model Zebrafish.Chemical research in toxicology · 2022Article
- A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment.Brain sciences · 2022Article
- New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning.iScience · 2022Article
- Resting-State Functional Magnetic Resonance Image to Analyze Electrical Biological Characteristics of Major Depressive Disorder Patients with Suicide Ideation.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
Abstract
Fusing multi-modality data is crucial for accurate identification of brain disorder as different modalities can provide complementary perspectives of complex neurodegenerative disease. However, there are at least four common issues associated with the existing fusion methods. First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities. Second, most existing methods often make prediction based on a single classifier, which might not be able to address the heterogeneity of the Alzheimer's disease (AD) progression. Third, many existing methods often employ feature selection (or reduction) and classifier training in two independent steps, without considering the fact that the two pipelined steps are highly related to each other. Forth, there are missing neuroimaging data for some of the participants (e.g., missing PET data), due to the participants' "no-show" or dropout. In this paper, to address the above issues, we propose an early AD diagnosis framework via novel multi-modality latent space inducing ensemble SVM classifier. Specifically, we first project the neuroimaging data from different modalities into a latent space, and then map the learned latent representations into the label space to learn multiple diversified classifiers. Finally, we obtain the more reliable classification results by using an ensemble strategy. More importantly, we present a Complete Multi-modality Latent Space (CMLS) learning model for complete multi-modality data and also an Incomplete Multi-modality Latent Space (IMLS) learning model for incomplete multi-modality data. Extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our proposed models outperform other state-of-the-art methods.
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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.