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基于病理学家注释的注意力诱导,以改善整个幻灯片的病理学图像分类器.

Ryoichi Koga1, Tatsuya Yokota1, Koji Arihiro2

  • 1Department of Computer Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya-shi, Aichi 466-8555, Japan.

Journal of pathology informatics
|January 23, 2025
PubMed
概括

注意诱导通过使用病理学家注释将注意力引导到相关区域来改进整个幻灯片图像 (WSI) 分类器. 这提高了病变分类的准确性,而不需要更多的训练数据.

关键词:
引起注意的诱导感应分类 分类 分类 分类.计算病理学计算病理学整个幻灯片图像的图像.

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科学领域:

  • 数字病理学数字病理学
  • 计算病理学计算病理学
  • 机器学习在医学中的应用

背景情况:

  • 整体幻灯片图像 (WSI) 的分类需要识别诊断相关的区域.
  • 多个实例学习和层次表示学习中的注意力机制旨在专注于这些区域.
  • 使用有限的WSI数据培训注意力机制往往会导致对信息领域的关注度不足.

研究的目的:

  • 提出和评估一种新的注意力诱导方法,以增强WSI分类器中的注意力机制.
  • 改善注意力机制的重点,以诊断相关区域的病变分类.
  • 提高WSI分类性能,而不需要增加培训WSI的数量.

主要方法:

  • 开发了一种专为等级式WSI表示量身定制的注意感应方法.
  • 利用病理学家的粗略注释来指导注意力机制.
  • 将注意力诱导方法集成到现有的WSI分类框架中.

主要成果:

  • 提出的注意力诱导方法显著改善了注意力机制的性能.
  • 在应用注意力诱导技术后,观察到WSI分类准确度的提高.
  • 该方法有效地引导注意力集中在诊断上相关的区域.

结论:

  • 引起注意力是一种可行的策略,可以增强WSI分类中的注意力机制.
  • 这种方法提供了一种解决方案,可以在有限的训练数据下提高分类器的性能.
  • 这种方法有望改善数字病理学的自动化分析.