细胞核的半监督语义细分与扩散模型和协作学习
Zhuchen Shao1, Sourya Sengupta1, Mark A Anastasio1,2
1University of Illinois Urbana-Champaign, Department of Electrical and Computer Engineering, Urbana, Illinois, United States.
Journal of medical imaging (Bellingham, Wash.)
|March 24, 2025
概括
这项研究引入了一种新的半监督学习框架,用于使用隐性扩散模型 (LDM) 和变压器解码器进行细胞核细分. 该方法有效地对细胞核进行细分,即使具有有限的标记数据,性能优于现有的方法.
科学领域:
- 生物医学图像分析
- 计算病理学计算病理学
- 机器学习用于医学成像.
背景情况:
- 自动化的细胞核细分对于疾病诊断和组织分析至关重要.
- 获得大型标记数据集用于监督学习是具有挑战性的.
- 半监督方法通过利用未标记的数据提供了一个解决方案.
研究的目的:
- 开发一个有效的半监督学习框架,用于细胞核细分.
- 为了应对极其有限的标记数据或各种注释类型所带来的挑战.
- 为了提高分段性能,在分销和分销之外 (OOD) 数据上.
主要方法:
- 引入了一种半监督框架,将隐性扩散模型 (LDM) 与基于变压器的解码器相结合.
- 利用各种未标记的数据集进行无监督的LDM培训,用于特征提取.
- 采用了一种连续的培训策略,并探索了一种协作式学习方法.
- 该框架,DTSeg,支持多道输入.
主要成果:
- 拟议的DTSeg框架在四个不同的数据集上显著优于现有的半监督和监督方法.
- 在不同的细胞类型和不同数量的标记数据中实现一致的性能.
- 在分销和OOD场景中表现出强大的概括能力.
结论:
- 通过整合无监督的LDM培训,DTSeg框架有效地通过有限的标记数据进行细胞核细分.
- 协作学习增强了概括性,在各种数据集中取得了卓越的结果.
- 该方法表现出强大的适应性和对各种数据条件的概括性.
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