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快速DDPM:用于医学图像对图像生成的快速否定扩散概率模型

Hongxu Jiang, Muhammad Imran, Teng Zhang

IEEE journal of biomedical and health informatics

|April 28, 2025

概括

通过减少时间步骤,Fast-DDPM显著加快了使用无声扩散概率模型 (DDPMs) 的医疗图像生成. 这种方法可以提高训练和采样速度,同时提高图像质量.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

拒绝扩散概率模型 (DDPMs) 在计算机视觉中显示出很大的前景,但由于计算成本高,在医学成像中未得到充分使用.
在DDPM中,大量的时间步骤 (例如1000步) 会导致长时间的训练 (数天/数周) 和医学图像采样 (数分钟/数小时).

研究的目的:

引入Fast-DDPM,一种提高医学成像培训速度,采样速度和生成质量的高效方法.
解决阻碍DDPM在临床应用中的应用的计算挑战.

主要方法:

开发了Fast-DDPM,一种新的方法,只需10个时间步骤进行培训和采样.
引入了两个高效的10步噪声调度器:统一和非统一的时间步骤采样.
通过调整培训和采样程序,优化了时间步骤的利用.

主要成果:

在医疗图像对图像任务中,快速DDPM比标准DDPM和其他最先进的方法取得了更高的性能.
与DDPM相比,训练时间 (0.2×) 和采样时间 (0.01×) 显著减少.
成功地将Fast-DDPM应用于多图像超分辨率,无噪声和图像对图像翻译.

结论:

快速DDPM提供了一个计算高效和有效的解决方案,用于医疗图像生成使用扩散模型.
该方法有可能通过更快,更高质量的医学成像来推进疾病诊断和治疗规划.

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