魔鬼在频率:受约束和适应细粒度域扰动,用于强大的医疗细分
IEEE journal of biomedical and health informatics
|June 9, 2025
概括
医学成像中的域泛化通过自适应双空间光谱扰乱 (AdaDSP) 框架得到了改进. 通过自适应地扰乱光谱频率来克服域移位,AdaDSP提高了不同系统的诊断准确性.
科学领域:
- 医学图像分析 医学图像分析
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 域泛化 (DG) 对于在各种医疗保健系统中可靠的医疗诊断至关重要.
- 从成像协议和设备的变化转移的域阻碍了准确的解剖识别.
- 目前的数据增强方法在没有扭曲解剖特征的情况下努力弥合领域差距.
研究的目的:
- 引入适应式双空间光谱扰乱 (AdaDSP) 框架,以应对医学成像领域的域概括性挑战.
- 增强训练数据的多样性,捕获阻碍一般化的敏感频段.
- 鼓励学习域不变表示,同时保持歧视能力.
主要方法:
- AdaDSP将可学习的光谱扰动注入输入图像和特征地图中,以进行广泛的增强.
- 一个细粒度光谱扰动模块使用注意力机制来调节频率分布,并适应性地扰乱敏感频段.
- 一个通用的三级语义约束框架促进了域不变的学习.
主要成果:
- 拟议的AdaDSP框架显著提高了数据多样性,并捕获了关键频段.
- 细粒度光谱扰动模块有效调节频率分布.
- 全球三级语义约束框架有助于学习强大的,域不变表示.
结论:
- AdaDSP的性能优于医疗图像分析领域的最先进方法.
- 该框架在两个医学成像任务中实现了2.40%和2.99%的显著改进.
- AdaDSP提供了一个有前途的解决方案,用于改善跨不同医疗保健机构的诊断一致性.
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