半监督前列腺细分的分离协作学习与多站点异质的未标记的MRI数据
Zhe Xu1, Donghuan Lu2, Jie Luo3
1Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China.
Medical image analysis
|February 4, 2024
概括
这项研究引入了医疗图像细分中半监督学习的新框架,解决了多个机构的数据稀缺性和异质性. 该方法有效地利用了本地和多站点的未标记数据,以改善前列腺MRI细分.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 通过MRI进行前列腺细分对于癌症分期和治疗计划至关重要.
- 半监督学习 (SSL) 由于有限的标记数据,对医学图像具有吸引力.
- 跨多个站点的数据异质性挑战了现有的SSL方法.
研究的目的:
- 为半监督前列腺细分使用多站点未标记的MRI数据提出一个新的框架.
- 在协作医学图像分析中应对数据稀缺性和异质性的挑战.
- 为了提高SSL在多中心设置中的通用性和稳定性.
主要方法:
- 引入了基于教师-学生模式的分离协作学习 (SCL) 框架.
- 实施本地学习策略,包括伪标签和循环传播的真实标签学习.
- 集成的外部多站点学习与相互依赖和对抗性干扰稳定性学习.
主要成果:
- 该SCL框架有效地将半监督学习泛化为多站点未标记的MRI数据.
- 与现有的半监督细分方法相比,实现了显著的性能改进.
- 在多个中心展示了多类心脏MRI细分的可扩展性.
结论:
- 拟议的SCL框架提供了一个强大的解决方案,用于半监督的医疗图像细分,使用多站点未标记的数据.
- 这种方法有效地克服了协作学习场景中的数据异质性挑战.
- 该方法有望通过增强的MRI细分来改善前列腺癌分期和治疗计划.
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