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使用ComBat进行深度功能批次校正,用于计算机病理学中的机器学习应用程序.

Pierre Murchan1,2, Pilib Ó Broin2,3, Anne-Marie Baird4

  • 1Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin D08 W9RT, Ireland.

Journal of pathology informatics
|October 14, 2024
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概括
此摘要是机器生成的。

在数字病理学AI模型中,ComBat协调有效减少批量效应,防止误诊. 这种方法确保可靠的性能估计,并保留真正的组织学信号,以便更好地泛化.

关键词:
人工智能的人工智能是人工智能.批量效应 批量效应 批量效应计算病理学计算病理学组织病理学 组织病理学癌症基因组图谱 (TCGA) 是一个

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科学领域:

  • 数字病理学数字病理学
  • 人工智能 (AI) 是一种人工智能.
  • 计算病理学计算病理学

背景情况:

  • 数字病理学的AI模型开发需要大型的多源数据集.
  • 人工智能模型风险学习混网站特定特征,导致过高估计的性能和糟糕的概括性.
  • 网站特定的特征可能会导致AI驱动的病理学分析中的潜在误诊.

研究的目的:

  • 评估ComBat协调在减轻数字病理学AI模型的批量效应方面的有效性.
  • 评估ComBat协调对组织源部位 (TSS),临床属性和遗传特征的预测的影响.
  • 确保可靠的性能估计,并提高人工智能模型的通用性,这些模型以多机构的全幻灯片图像 (WSIs) 进行训练.

主要方法:

  • 从癌症基因组图谱 (TCGA) 结肠 (COAD) 和胃腺癌数据集的全幻灯片图像 (WSI) 被利用.
  • 使用三个特征提取模型生成了补丁嵌入,随后与ComBat.Bat进行了协调.
  • 基于注意力的多实例学习模型被训练来预测组织源部位 (TSS),临床和遗传属性,使用原始,正常化和协调嵌入.

主要成果:

  • 康巴协同显著降低了组织源部位 (TSS) 预测准确度 (AUROC降至~0.5),表明成功缓解了批量效应.
  • 与TSS相关的临床属性的可预测性,如种族和治疗反应,在协调后下降.
  • 遗传特征的预测,如MSI状态,在ComBat协调后仍然很强大,保留了真正的组织学信号.

结论:

  • ComBat协调有效地降低了人工智能模型学习WSIs中混特征的风险.
  • 这种方法确保了数字病理学AI模型的更可靠的性能估计.
  • 对于整合大规模的数字病理学数据集,ComBat协调是有前途的,提高了模型的通用性和可靠性.