ArticleBrain sciences2020
Multi-View Based Multi-Model Learning for MCI Diagnosis.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population.Brain and behavior · 2023Trial
- Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.The journal of prevention of Alzheimer's disease · 2025Article
- Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases.Human brain mapping · 2023Article
- Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network.Biomedical engineering online · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).
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Registered trials
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