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训练免疫类型深度学习模型与同段地面真实细胞标签衍生方法的训练提高了虚拟染色准确度.

Abu Bakr Azam1, Felicia Wee2, Juha P Väyrynen3

  • 1School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore.

Frontiers in immunology
|July 15, 2024
PubMed
概括
此摘要是机器生成的。

在深度学习模型中使用同段细胞标签显著提高了H&E染色组织中的免疫类型精度,优于肺癌患者分层的连续切割方法.

关键词:
CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD3 CD4 CD4 CD4 CD4 CD4 CD5 CD5 CD5 CD5 CD6 CD7 CD7 CD7 CD7 CD7 CD7 CD7 CD7 CD8 CD7 CD8 CD8 CD7 CD8 CD8 CD8 CD8 CD9在Pix2Pix生成对抗网络 (P2P-GAN) 中.深度学习是一种深度学习.地基真相细胞标签标签血素和乙素 (H&E) 的使用瘤透性淋巴细胞 (TILs) 是一种虚拟染色是一种虚拟染色.

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 发现生物标志物的发现.

背景情况:

  • 深度学习 (DL) 模型预测H&E染色组织中的生物标志物表达,有助于癌症研究和治疗的多标志物免疫类型.
  • 目前的DL模型经常使用邻近的IHC染色部分的细胞标签,这些标签可能不如同一部分的标签准确.

研究的目的:

  • 评估细胞标签衍生方法对DL模型性能对H&E染色组织分析的影响.
  • 用Pix2Pix生成对抗网络 (P2P-GANs) 来比较"相同部分"标签方法与"串行部分"方法.

主要方法:

  • 开发并比较了两个P2P-GAN虚拟染色模型,用于肺癌H&E图像中的CD3+T细胞预测.
  • 一种模型使用来自同一组织部分的地面真实标签,而另一种模型使用来自相邻串行部分的标签.

主要成果:

  • 与"串行部分"模型相比",同一部分"DL模型显示出明显更高的预测性能.
  • "相同部分"模型有效地通过公共队列中的生存结果对肺癌患者进行了分层,表明了临床相关性.

结论:

  • 来自同一组织部分的基准真实细胞标签增强了基于DL的免疫型化解决方案的性能.
  • "相同部分"方法为数字病理学中的生物标志物预测提供了更准确和临床适用的方法.