Survey on the Applications of Generative Models in Medical Image Analysis

Corresponding author: zhangzhicheng

DOI: 10.12201/bmr.202511.00066

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  Abstract: Abstract Purpose/Significance Medical image analysis lies at the core of precision medicine. However, the scarcity of high-quality annotated data and domain shifts across imaging devices have long hindered its advancement. Deep generative models, with their powerful capacity for modeling complex data distributions, provide a key technical pathway to overcome these limitations. This paper aims to systematically review the current state, cutting-edge progress, and core challenges of generative models in medical image analysis, offering a comprehensive technological landscape and forward-looking guidance for future research.Method/Process This study adopts a literature review methodology. First, it systematically elaborates on the fundamental principles, technical evolution, and advantages and limitations of mainstream generative models, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Diffusion Models. Second, from the perspectives of key application tasks—such as cross-modality image synthesis, data augmentation, image reconstruction and denoising, super-resolution, segmentation, and detection—it provides a detailed summary and classification of existing research efforts. Finally, the paper reviews evaluation frameworks for model performance, integrating a multidimensional assessment system that spans from technical indicators to clinical utility. Result/Conclusion The findings indicate that generative models have demonstrated remarkable potential and application value across multiple critical tasks in medical image analysis. Nevertheless, clinical translation remains constrained by issues such as insufficient controllability and interpretability, limited generalization and robustness, data ethics concerns, and high computational costs. The study concludes that future breakthroughs will depend on developing trustworthy and controllable medical foundation models, deepening multimodal data integration, and establishing standardized evaluation benchmarks and protocols. This comprehensive review provides theoretical insight and directional guidance for advancing the healthy development and clinical implementation of generative models in the medical domain.

  Key words: Medical Image Analysis; GANs; VAEs; Diffusion Models；

  Submit time: 21 November 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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