ArticleScientific reports2023
A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer's Disease: A Preprocessing-Focused Pipeline.Journal of imaging · 2026Article
- AI alignment in medical imaging: Unveiling hidden biases through counterfactual analysis.PLOS digital health · 2026Article
- Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and Diagnosis.Ophthalmology science · 2026Article
- Anatomically guided latent diffusion for high-resolution 3D chest CT synthesis.Scientific reports · 2026Article
- Modelling Stochastic Sensor Noise via Mask-Conditioned Diffusion for Data Augmentation in Low-SNR LGE-CMR.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence-generated synthetic data for cancer research and clinical trials.Nature reviews. Cancer · 2026Review
- A multi-level segmentation-guided diffusion model for streak artifact reduction in routine non-contrast chest CT.Medical & biological engineering & computing · 2026Article
- AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization.Scientific reports · 2026Article
- Unconditional latent diffusion models memorize patient imaging data.Nature biomedical engineering · 2026Article
- Conditional generative diffusion model for 3D trabecular bone synthesis with tunable microstructure.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- A Systematic Review of Diffusion Models for Medical Image-Based Diagnosis: Methods, Taxonomies, Clinical Integration, Explainability, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Few-shot deployment of pretrained MRI transformers in brain imaging tasks.Frontiers in artificial intelligence · 2026Article
- Toward Digital Twins for Optimal Radioembolization.PET clinics · 2026Review
- Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data.Research square · 2025Article
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Progressive Disease Image Generation with Ordinal-Aware Diffusion Models.Diagnostics (Basel, Switzerland) · 2025Article
- TransUNET-DDPM: A transformer-enhanced diffusion model for subject-specific brain network generation and classification.Computers in biology and medicine · 2025Article
- MedIENet: medical image enhancement network based on conditional latent diffusion model.BMC medical imaging · 2025Article
- Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Tasks.Research square · 2025Article
5 more citing papers are in PubMed but not listed here.
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12 authors.
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Abstract
Although generative adversarial networks (GANs) can produce large datasets, their limited diversity and fidelity have been recently addressed by denoising diffusion probabilistic models, which have demonstrated superiority in natural image synthesis. In this study, we introduce Medfusion, a conditional latent DDPM designed for medical image generation, and evaluate its performance against GANs, which currently represent the state-of-the-art. Medfusion was trained and compared with StyleGAN-3 using fundoscopy images from the AIROGS dataset, radiographs from the CheXpert dataset, and histopathology images from the CRCDX dataset. Based on previous studies, Progressively Growing GAN (ProGAN) and Conditional GAN (cGAN) were used as additional baselines on the CheXpert and CRCDX datasets, respectively. Medfusion exceeded GANs in terms of diversity (recall), achieving better scores of 0.40 compared to 0.19 in the AIROGS dataset, 0.41 compared to 0.02 (cGAN) and 0.24 (StyleGAN-3) in the CRMDX dataset, and 0.32 compared to 0.17 (ProGAN) and 0.08 (StyleGAN-3) in the CheXpert dataset. Furthermore, Medfusion exhibited equal or higher fidelity (precision) across all three datasets. Our study shows that Medfusion constitutes a promising alternative to GAN-based models for generating high-quality medical images, leading to improved diversity and less artifacts in the generated images.
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