Application progress of generative network in medical image generation
10.3760/cma.j.cn121382-20250417-00031
- VernacularTitle:生成式网络在医学图像生成中的应用进展
- Author:
Zhimin XU
1
;
Shiyuan LIU
Author Information
1. 上海理工大学健康科学与工程学院，上海 200093
- Keywords:
Computed tomography;
Magnetic resonance imaging;
Image generation;
Generative network;
Variational autoencoder;
Generative adversarial network;
Denoising di
- From:
International Journal of Biomedical Engineering
2025;48(4):401-406
- CountryChina
- Language:Chinese
-
Abstract:
The generative network shows great potential for application in the field of medical imaging, as it can effectively address the scarcity of medical image data and the difficulty of labeling. In this review, the representative studies on mainstream generative networks in multimodal image synthesis, low-dose image reconstruction and structure preservation in recent years were reviewed. This includes an overview of the evolution of frameworks such as variational autoencoder, generative adversarial network and denoising diffusion probabilistic model, as well as their application in medical image generation. The current challenges and future research directions were also discussed in order to provide technical support for the clinical implementation of medical image generation methods.
