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基于隐私保护的潜伏扩散合成医疗图像生成

Yongyi Shi, Wenjun Xia, Chuang Niu

IEEE transactions on medical imaging

|October 6, 2025

概括

这项研究引入了一种新的潜在扩散模型,用于生成合成医疗图像 (CT,MRI,PET),而不会损害患者的隐私. 在这种合成数据上训练深度学习模型可以与使用原始数据产生类似的结果,从而实现安全的数据共享.

科学领域:

人工智能的人工智能
医疗成像医学成像
数据隐私数据隐私数据

背景情况:

深度学习在医学成像方面表现出色,但需要大量数据集,构成隐私风险和共享挑战.
扩散模型是先进的AI生成模型,可以实现显著的结果.

研究的目的:

开发一种保护隐私的隐藏扩散方法,用于合成医学图像生成.
为深度学习模型开发提供大型医疗数据集的安全共享.

主要方法:

开发了一个潜在的扩散模型来生成合成CT,MRI和PET图像.
该模型包含一个有效保护患者隐私的保障机制.
最先进的告密/超级解决网络在生成的合成数据上接受了培训.

主要成果:

潜伏扩散模型生成的合成数据允许训练深度学习网络.
使用合成数据获得的图像质量与使用原始数据获得的图像质量相当.
嵌入式保护机制有效地保护了患者的隐私.

结论:

拟议的潜在扩散方法使各种大型医疗数据集的隐私保护共享成为可能.
这有助于开发深度学习模型和数据层面的潜在联合学习.

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