DP-SSLoRA:一个保护隐私的医疗分类模型,将差异性隐私与自我监督的低级适应结合起来
Chaokun Yan1, Haicao Yan2, Wenjuan Liang1
1School of Computer and Information Engineering, Henan University, Kaifeng, 475004, Henan, China; Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, Henan, China; Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, 475004, Henan, China.
Computers in biology and medicine
|July 4, 2024
概括
这项研究介绍了DP-SSLoRA,这是一种用于保护隐私的医疗图像分析的新方法. 它平衡了患者的隐私与模型的实用性,在胸部X射线分类任务中实现了高精度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 患者隐私问题限制了医疗保健中深度学习的使用.
- 差异隐私 (DP) 通过添加噪音提供了一个解决方案,但与高维医学数据和有限样本作斗争.
- 在医疗AI中平衡隐私和实用性仍然是一个挑战.
研究的目的:
- 开发一个保护隐私的医疗图像分类模型.
- 在医疗应用中增强差异化私有模型的实用性.
- 解决医疗深度学习中隐私和准确性之间的权衡问题.
主要方法:
- 拟议的DP-SSLoRA,将差异性隐私与自我监督的低级适应相结合.
- 使用未标记数据的自主监督预训,以实现强大的特征表示.
- 使用低级分解来减轻DP噪声对分类任务的影响.
主要成果:
- 在三个胸部X射线数据集上,DP-SSLoRA表现出强大的性能和隐私保证.
- 实现了高的曲线下面积 (AUC) 评分:0.942 (RSNA),0.9658 (Covid-QU-mini) 和0.9886 (胸部X射线15k) 在e=2.
- 在现实世界医学成像场景中验证了模型的有效性.
结论:
- 在医学图像分类中,DP-SSLoRA有效地平衡了隐私和实用性.
- 这项研究为在医学中开发保护隐私的人工智能提供了有价值的见解.
- 源代码可用于进一步的研究和应用.
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