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无监督医疗图像翻译与对抗性扩散模型

Muzaffer Ozbey, Onat Dalmaz, Salman U H Dar

    IEEE transactions on medical imaging
    |June 28, 2023
    PubMed
    概括

    新的对抗性扩散模型SynDiff通过提高样本保真度来增强医疗图像翻译. 这种方法在像MRI-CT翻译这样的任务中比生成对抗网络 (GAN) 提供了更高的性能.

    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:

    • 医疗成像协议可以通过源到目标模式翻译来赋予缺失的数据.
    • 生成对抗网络 (GAN) 通常用于合成目标图像,但可以具有有限的样本保真度.
    • 现有的方法与图像分布的隐性表征作斗争.

    研究的目的:

    • 介绍SynDiff,一种用于增强医疗图像翻译的新型对抗性扩散建模方法.
    • 为了提高样本的真实性和合成目标医疗图像的性能.
    • 为实现对双边模式翻译的未配对数据集的培训.

    主要方法:

    • SynDiff使用条件扩散过程将噪声和源图像映射到目标图像上,直接捕获图像分布.
    • 大的扩散步骤与反向扩散方向的对立投影,确保快速和准确的推断.
    • 一个循环一致的架构,与合的扩散和非扩散模块相结合,方便对未配对数据进行训练.

    主要成果:

    • 与竞争中的GAN和扩散模型相比,SynDiff在数量和质量上表现出优越的性能.
    • 对多对比MRI和MRI-CT翻译任务进行了广泛的评估.
    • 拟议的方法在医学图像合成中实现了更好的样本真实性.

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    结论:

    • 在SynDiff中实现的对抗性扩散建模,为医学图像翻译提供了强大的方法.
    • SynDiff克服了传统GAN在医学图像合成的样本保真度方面的局限性.
    • 该方法显示了改善医学成像协议的多样性和归算的巨大潜力.