ArticlePloS one2019
Neuroimaging modality fusion in Alzheimer's classification using convolutional neural networks.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses 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
19 citing papers in PubMed, 2 syntheses or guidelines 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
- An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.BioMed research international · 2022Pooled it
- Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.PloS one · 2025Article
- Structure-aware medical image fusion via mean curvature enhancement in the contourlet domain.PloS one · 2025Article
- Article
- Alzheimer's disease detection using data fusion with a deep supervised encoder.Frontiers in dementia · 2024Article
- Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning.Scientific reports · 2022Article
- Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI.Annals of translational medicine · 2022Article
- The role of the deep convolutional neural network as an aid to interpreting brain [European radiology · 2021Article
- Artificial intelligence for molecular neuroimaging.Annals of translational medicine · 2021Review
- Application and Construction of Deep Learning Networks in Medical Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
- An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis.Frontiers in digital health · 2021Article
- Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.Computational and mathematical methods in medicine · 2021Article
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
- Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes.Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings · 2020Article
- Article
- A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease.Frontiers in aging neuroscience · 2020Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and FDG PET, but a comprehensive and balanced comparison of the MRI and amyloid PET modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with identical neural network architectures. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.
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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.