ArticleScientific data2024
Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- FairGen: preference-aligned diffusion for demographically equitable medical image synthesis.NPJ digital medicine · 2026Article
- Privacy-Preserving Latent Diffusion-Based Synthetic Medical Image Generation.IEEE transactions on medical imaging · 2026Article
- Unconditional latent diffusion models memorize patient imaging data.Nature biomedical engineering · 2026Article
- BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification.Scientific data · 2026Article
- Potential of Image2Image translation in reducing AI bias attributed to differences in CT reconstruction methods: proof-of-concept study on a paired dataset.Frontiers in radiology · 2026Article
- Adaptive 3D Augmentation in StyleGAN2-ADA for High-Fidelity Lung Nodule Synthesis from Limited CT Volumes.Sensors (Basel, Switzerland) · 2025Article
- A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain.Scientific data · 2025Article
- Dual-Stream Contrastive Latent Learning Generative Adversarial Network for Brain Image Synthesis and Tumor Classification.Journal of imaging · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- MR electrical properties mapping using vision transformers and canny edge detectors.Magnetic resonance in medicine · 2025Article
- Denoising diffusion model for increased performance of detecting structural heart disease.medRxiv : the preprint server for health sciences · 2024Article
- Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context.IEEE transactions on medical imaging · 2024Article
- Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.Bioengineering (Basel, Switzerland) · 2024Article
- On the use of synthetic data for healthcare AI in Africa: Technical performance, governance challenges, and policy readiness.Digital healthReview
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Authors and funding
4 authors.
Funding
Abstract
Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%-90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.
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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.