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S2L-CM:使用对比规范化和像素级多个实例学习,在组织病理学图像中进行草图监督的核细分.

Hyun-Jic Oh1, Seonghui Min1, Won-Ki Jeong1

  • 1Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea.

Computers in biology and medicine
|May 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了S2L-CM,这是一种使用涂标签进行核细分的新框架. 它有效地产生伪标签,改善深度学习模型培训,而没有完整的基础真相数据.

关键词:
多尺度的对比性规范化规范化像素级多个实例学习.伪标签监督 伪标签监督弱监督的核细分是核的细分.

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科学领域:

  • 数字病理学数字病理学
  • 计算生物学是一种计算生物学.
  • 医疗图像分析 医疗图像分析

背景情况:

  • 深度学习在病理核细分方面表现出色,但需要广泛的手动标签.
  • 监督学习需要大量的精力来生成基本真相数据.
  • 弱监督的学习提供了稀疏注释的解决方案,但通常会产生较低的绩效.

研究的目的:

  • 提出S2L-CM,一个涂监督的核细分框架.
  • 为了减少在核细分中手动使用地面真实标签的需求.
  • 为了提高细分性能,使用精致的伪标签.

主要方法:

  • 从稀疏的涂注释中利用自我生成的伪标签进行模型培训.
  • 使用多尺度对比规范化用于伪标签改进.
  • 采用像素级多实例学习来提高细分精度.

主要成果:

  • 在四个核数据集中,S2L-CM证明了有效性和稳定性.
  • 与最先进的技术相比,拟议的方法实现了具有竞争力的性能.
  • 在不需要完整的基本真相标签的情况下实现了成功的核细分.

结论:

  • 通过最大限度地减少手动注释工作,S2L-CM提供了一种高效的核细分方法.
  • 该框架对数字病理学中的实际应用充满希望.
  • 进一步的研究可以建立在这种方法上,用于高级弱监督的细分任务.