ArticleJournal of neuroscience methods2020
Deep residual learning for neuroimaging: An application to predict progression to Alzheimer's disease.
Article in Journal of neuroscience methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.
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Who cites it
56 citing papers in PubMed, 2 syntheses or guidelines pooled it, 173 citations in OpenAlex.
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled 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
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- Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer's Disease Diagnosis and Multi-Modal Biomarker Identification.Interdisciplinary sciences, computational life sciences · 2026Article
- Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using a Vision Transformer and Hippocampal MRI Slices.Bioengineering (Basel, Switzerland) · 2026Article
- Generating MRI-Derived CSF Proxy-Markers to Predict and Visualize Alzheimer's Disease Progression.Human brain mapping · 2025Article
- AI-powered model for accurate prediction of MCI-to-AD progression.Acta pharmaceutica Sinica. B · 2025Article
- A novel interpreted deep network for Alzheimer's disease prediction based on inverted self attention and vision transformer.Scientific reports · 2025Article
- Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.Alzheimer's research & therapy · 2025Article
- An artificial intelligence-based framework for Alzheimer's disease diagnosis from magnetic resonance imaging volumes via video vision transformer.Intelligent medicine · 2025Article
- Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.The journal of prevention of Alzheimer's disease · 2025Article
- Cognitive performance classification of older patients using machine learning and electronic medical records.Scientific reports · 2025Article
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Comparison of machine learning algorithms for automatic prediction of Alzheimer disease.Journal of the Chinese Medical Association : JCMA · 2025Article
- Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer's Disease Classification.Bioengineering (Basel, Switzerland) · 2025Article
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
- Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer's disease continuum.Network neuroscience (Cambridge, Mass.) · 2025Article
- Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.Frontiers in artificial intelligence · 2025Article
- An MRI based histogram oriented gradient and deep learning approach for accurate classification of mild cognitive impairment and Alzheimer's disease.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe unparalleled performance of deep learning approaches in generic image processing has motivated its extension to neuroimaging data. These approaches learn abstract neuroanatomical and functional brain alterations that could enable exceptional performance in classification of brain disorders, predicting disease progression, and localizing brain abnormalities. NEW
methodThis work investigates the suitability of a modified form of deep residual neural networks (ResNet) for studying neuroimaging data in the specific application of predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Prediction was conducted first by training the deep models using MCI individuals only, followed by a domain transfer learning version that additionally trained on AD and controls. We also demonstrate a network occlusion based method to localize abnormalities.
resultsThe implemented framework captured non-linear features that successfully predicted AD progression and also conformed to the spectrum of various clinical scores. In a repeated cross-validated setup, the learnt predictive models showed highly similar peak activations that corresponded to previous AD reports. COMPARISON WITH EXISTING
methodsThe implemented architecture achieved a significant performance improvement over the classical support vector machine and the stacked autoencoder frameworks (p < 0.005), numerically better than state-of-the-art performance using sMRI data alone (> 7% than the second-best performing method) and within 1% of the state-of-the-art performance considering learning using multiple neuroimaging modalities as well.
conclusionsThe explored frameworks reflected the high potential of deep learning architectures in learning subtle predictive features and utility in critical applications such as predicting and understanding disease progression.
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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.