ArticleAlzheimer's research & therapy2021
Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning.
Article in Alzheimer's research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 78 citations in OpenAlex.
- Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study.Human brain mapping · 2023Pooled it
- Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled it
- Early diagnosis of Alzheimer's disease based on brain morphological changes: A comprehensive approach combining voxel-based morphometry and deep learning.Neuroimage. Reports · 2026Article
- Scoping the Landscape of Deep Learning for Alzheimer's Disease Stage Classification: Methods, Challenges, and Opportunities.BME frontiers · 2025Review
- Deep Convolutional Neural Network for Dedicated Regions-of-Interest Based Multi-Parameter Quantitative Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of the Knee Joint.Journal of imaging informatics in medicine · 2024Article
- Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification.Scientific reports · 2024Article
- Study on the Relationship between Cerebral Blood Perfusion, Neuronal Cytokines and Cognitive Function in Patients with Alzheimer's Disease.Actas espanolas de psiquiatria · 2024Article
- Article
- A review of the applications of generative adversarial networks to structural and functional MRI based diagnostic classification of brain disorders.Frontiers in neuroscience · 2024Review
- Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals.IEEE access : practical innovations, open solutions · 2024Article
- Generative artificial intelligence model for simulating structural brain changes in schizophrenia.Frontiers in psychiatry · 2024Article
- A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer's Dementia Detection.Neuropsychiatric disease and treatment · 2024Review
- Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning.Neural regeneration research · 2023Review
- Machine Learning in Clinical Trials: A Primer with Applications to Neurology.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- AHANet: Adaptive Hybrid Attention Network for Alzheimer's Disease Classification Using Brain Magnetic Resonance Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.Human brain mapping · 2023Article
- A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.Neural computing & applications · 2023Review
- Generative Adversarial Networks in Brain Imaging: A Narrative Review.Journal of imaging · 2022Review
- New evidence on technological acceptance model in preschool education: Linking project-based learning (PBL), mental health, and semi-immersive virtual reality with learning performance.Frontiers in public health · 2022Article
- Artificial intelligence applications in psychoradiology.Psychoradiology · 2021Article
Corrections and comments
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9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundGenerative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer's disease (AD) classification performance.
methodsT1-weighted brain MRI scans from 151 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer's Coordinating Center (NACC, n = 565) were used for model validation.
resultsThe 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets.
conclusionThis study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.
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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.