ArticleBrain informatics2020
GAN-based synthetic brain PET image generation.
Article in Brain informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 5 of them syntheses that pooled it.
What it found
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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
39 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.Journal of digital imaging · 2022Pooled 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
- Microbial Colony Image Enhancement Algorithm Based on Composite Adversarial D-DCGAN Neural Network.Microorganisms · 2026Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- The potential of generative AI with prostate-specific membrane antigen (PSMA) PET/CT: challenges and future directions.Medical review (2021) · 2025Review
- Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning.EJNMMI physics · 2025Article
- Scaling Synthetic Brain Data Generation.IEEE journal of biomedical and health informatics · 2025Article
- Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer's disease research.Frontiers in aging neuroscience · 2025Review
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.Medical physics · 2024Article
- GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis.Patterns (New York, N.Y.) · 2024Article
- Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.Frontiers in artificial intelligence · 2024Article
- DeepCGAN: early Alzheimer's detection with deep convolutional generative adversarial networks.Frontiers in medicine · 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
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer's disease-normal control (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD).
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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.