VP-SFDA:用于跨模态医疗图像的视觉快速源代码免费域调整
Yixin Chen1, Yan Wang2, Zhaoheng Xie1
1Institute of Medical Technology and National Biomedical Imaging Center, Peking University, Beijing 100191, China.
Health data science
|May 12, 2025
概括
这项研究引入了一种新的2阶段框架,视觉提示无源域调整 (VP-SFDA),通过使用视觉提示和拒绝技术来提高不同成像类型的医疗图像细分精度.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 无源无监管域适应 (SFUDA) 方法在医学成像中面临域转移的挑战.
- 现有的SFUDA方法依赖于伪标签的denoising,但随着更大的域间隙,它们的有效性会下降.
- 不可靠的伪标签阻碍了当域名转移显著时的目标域模型训练.
研究的目的:
- 为医疗成像中强大的SFUDA提出一个新的2阶段框架,VP-SFDA.
- 通过使用视觉提示,增强域名适应性,将目标域数据与源域分布连接起来.
- 通过优化适应过程,提高对目标域的细分模型性能.
主要方法:
- 引入了一个两阶段的框架:VP-SFDA.
- 阶段1:输入特定的视觉提示将目标域数据与源域分布连接起来,使用视觉提示和批量规范化进行对齐.
- 第二阶段:适应过程优化了使用denoising技术的细分模型.
主要成果:
- VP-SFDA框架评估了4个医学成像任务 (MRI到CT,CT到MRI用于腹部和心脏).
- VP-OS方法显著改善了腹部MRI对CT的适应 (DICE:0.658至0.773,ASD:3.489至2.961).
- 与基线算法相比,VP-LD和VP-DPL方法显示了相当大的收益.
结论:
- 在医学成像中,VP-SFDA为SFUDA提供了一种卓越的两阶段方法.
- 该框架有效地使用视觉提示和批量规范化来进行域调整和denoising,以提高适应性.
- 实验证实VP-SFDA的性能优于现有方法,验证了特定领域模式的好处.
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