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相关实验视频

Updated: May 8, 2025

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用于WSI的多实例学习:对基于注意力的方法进行比较分析.

Martim Afonso1, Praphulla M S Bhawsar2, Monjoy Saha2

  • 1Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon 1049-001, Portugal.

Journal of pathology informatics
|December 24, 2024
PubMed
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弱监督的多个实例学习 (MIL) 模型在数字病理学中显示出有前途的预测癌症表型和从整个幻灯片图像 (WSI) 中检测TP53突变. 这些人工智能方法可以在层面识别与癌症相关的特定细胞形态,帮助诊断.

科学领域:

  • 数字病理学数字病理学
  • 计算病理学计算病理学
  • 人工智能在医学中的应用

背景情况:

  • 整个幻灯片图像 (WSI) 在数字病理学中至关重要,但由于幻灯片级注释,对AI分析构成挑战.
  • 精确的癌症表型和与突变相关的层细胞形态的识别是重要的障碍.
  • 现有的弱监督的多阶段学习 (MIL) 方法需要对复杂的病理学任务进行进一步的调查.

研究的目的:

  • 为了比较注意力MIL (AMIL) 和添加剂MIL (AdMIL) 在瘤检测和TP53突变预测方面的疗效.
  • 评估MIL架构在识别与癌症和突变相关的层形态特征方面的能力.
  • 探索MIL模型在帮助病理学家的潜力,通过在高维的WSIs中突出关注区域 (ROI).

主要方法:

  • 实施和比较了两个监管较弱的MIL方法,即AMIL和AdMIL.
  • 模型在来自肺状细胞癌 (TCGA-LUSC) 和侵入性乳腺癌 (TCGA-BRCA) WSIs 的数据集上进行了训练和测试.
  • 分析包括在5×放大时检测瘤,并在5×,10×和20×放大时检测TP53突变.

主要成果:

  • 修改后的添加剂MIL在瘤检测方面实现了与参考实施方案 (AUC 0.96) 相似的性能,略低于AMIL (AUC 0.97).
关键词:
注意力机制注意力机制癌症 癌症 癌症 癌症多个实例的学习是多个实例的学习.这就是TP53的特点.整个幻灯片图像的图像.

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  • TP53突变检测对更高放大功能的特征更敏感,这表明细胞形态分辨率的重要性.
  • MIL架构展示了在不同放大度下识别对形态特征的独特敏感性的能力,突出了相关的ROI.

    • 结论:

    • 弱监督的MIL模型在数字病理学中对瘤检测和TP53突变预测都有效.
    • 更高的放大值对于检测与TP53突变等特定分子变化相关的微妙形态特征至关重要.
    • MIL模型的ROI识别能力为集成到数字病理学工作流程提供了巨大的潜力,提高了诊断效率.