ArticleTomography (Ann Arbor, Mich.)2018
Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images.
Article in Tomography (Ann Arbor, Mich.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.Briefings in bioinformatics · 2026Review
- The potential of generative AI with prostate-specific membrane antigen (PSMA) PET/CT: challenges and future directions.Medical review (2021) · 2025Review
- Evaluation of Perceptual Realism and Clinical Plausibility of AI-Generated Colon Polyp Images.Biomedicines · 2025Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
- DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks.BMC genomics · 2024Article
- Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset.European radiology · 2023Article
- Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images.Scientific reports · 2023Article
- Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images.Journal of personalized medicine · 2023Article
- Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network.Scientific reports · 2023Article
- Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks.Frontiers in neuroinformatics · 2023Article
- Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET.EJNMMI research · 2022Article
- Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review.Insights into imaging · 2022Review
- Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients.Scientific reports · 2022Article
- The role of generative adversarial networks in brain MRI: a scoping review.Insights into imaging · 2022Article
- Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning.Cancers · 2022Article
- A generative adversarial network for synthetization of regions of interest based on digital mammograms.Scientific reports · 2022Article
- Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.NMR in biomedicine · 2022Review
- Generative Adversarial Networks in Brain Imaging: A Narrative Review.Journal of imaging · 2022Review
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Assessing atypical brain functional connectivity development: An approach based on generative adversarial networks.Frontiers in neuroscience · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%-60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.
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