基于频谱干预的不变因果表示学习,用于单域的可概括的医疗图像细分
Wentao Liu1, Zhiwei Ni1, Xuhui Zhu2
1School of Management, Hefei University of Technology, Anhui 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui 230009, China.
Medical image analysis
|August 1, 2025
概括
这项研究引入了一种新的因果表示学习框架 (SI2CRL),以提高医疗图像细分性能,尽管域变化. 该方法通过从因果关系的角度统一数据生成和表示学习来提高稳定性.
科学领域:
- 医学图像分析 医学图像分析
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 由于收购变异导致的域名转移阻碍了细分模型的性能.
- 目前的方法集中在数据多样性或域不变表示,建模表面的统计依赖关系.
- 需要一种因果关系的视角来实现强大而可概括的细分.
研究的目的:
- 提出基于频谱干预的不变因果表示学习 (SI2CRL) 框架.
- 从因果角度统一数据生成和表示学习.
- 通过解决潜在的因果因素来实现域强大的细分.
主要方法:
- 将频率域中的对象元素作为数据生成的相变量.
- 采用基于振幅的干预模块用于低频扰动.
- 代表性学习的两阶段因果协同建模过程:因果脱和对抗性因果净化.
主要成果:
- 在各种医学成像任务中获得一致的性能增长:跨位前列腺MRI,跨模式腹部CT-MRI和跨序心脏MRI细分.
- 与最先进的方法相比,证明了优越的稳定性.
- 有效地过风格敏感的非因果因素,并推导出足够的因果信息.
结论:
- 拟议的SI2CRL框架有效地解决了医疗图像细分领域的转移.
- 因果表示学习提供了一种比传统方法更强大和更具普遍性的方法.
- SI2CRL为提高AI在医学成像中的可靠性提供了一个有希望的方向.
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