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医学图像创建和翻译的生成模型:一个范围审查
Haowen Pang1,2, Tiande Zhang1,3, Yanan Wu4
1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110016, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本综述探讨了用于医学图像创建和翻译的VAE,GAN和扩散模型等生成模型. 它分析了它们的优点,弱点和各种成像模式的应用,指导了未来的研究.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 生成模型对于医学成像技术的进步至关重要.
- 应用包括生成新图像和在模式之间翻译.
- 现有的审查缺乏对各种生成模型及其特定医学成像任务的全面分析.
研究的目的:
- 提供医学图像创建和翻译中的生成模型的学术审查.
- 分析变量自编码器 (VAE),生成对抗网络 (GAN) 和扩散模型 (DM) 的优缺点.
- 为了分类和检查医疗图像创建和翻译中的任务,可视化跨模式的翻译趋势.
主要方法:
- 对生成模型 (VAE,GAN,DM) 的文献综述和分析.
- 基于下游任务 (创建) 和目标模式 (翻译) 的研究分类.
- 使用和弦图可视化跨模式 (MRI,CT,X射线,PET,超声波) 和MRI对比机制的医学图像翻译.
主要成果:
- 生成模型为医疗图像合成和跨模式映射提供了多种功能.
- 分析揭示了VAE,GAN和DM在医学成像环境中的特定优势和局限性.
- 弦图说明了医疗图像翻译当前研究的景观,突出了关键的模式和翻译方向.
结论:
- 生成模型是医学图像创建和翻译的强大工具.
- 了解特定模型的优点和缺点是有效应用的关键.
- 未来的研究应该集中在增强医学成像解决方案的潜在途径和实施指南上.
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