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富里埃MIL:基于富里埃过的多个实例学习,用于整张幻灯片图像分析.

Yi Zheng1,2, Harsh Sharma1,2, Margrit Betke1

  • 1Department of Computer Science, Boston University, Boston, 02215 MA USA.

International journal of computer vision
|December 31, 2025
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概括
此摘要是机器生成的。

富丽埃MIL是一个新的多实例学习框架,有效地分析千兆像素全幻灯片图像用于数字病理学任务. 它在转移检测,肺癌分类和阿尔茨海默氏症病理识别方面优于现有的方法.

关键词:
计算病理学计算病理学数字病理学数字病理学富里叶变换是什么意思 富里叶变换多个实例的学习是多个实例的学习.

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科学领域:

  • 计算病理学计算病理学
  • 医学中的人工智能
  • 数字图像分析数字图像分析

背景情况:

  • 像CNN和变压器这样的计算机视觉技术提升了图像分类.
  • 数字病理学中的千兆像素全幻灯片图像 (WSIs) 由于尺寸和异质性而带来挑战.
  • 现有的方法难以应对WSI的规模和复杂性.

研究的目的:

  • 介绍FourierMIL,一个多实例学习框架,用于高效的WSI分析.
  • 利用离散的里埃转换来捕捉WSI中的全球和本地依赖关系.
  • 展示FourierMIL在各种数字染色和病理学任务中的适应性.

主要方法:

  • 开发了FourierMIL,一个无注意的多实例学习框架.
  • 利用离散的里埃变换从WSIs中提取特征.
  • 在转移检测 (CAMELYON16),肺癌分类 (TCGA,CPTAC) 和阿尔茨海默病病理识别 (UNITE,FHS,ADC) 上评估了FourierMIL.

主要成果:

  • 在所有测试的数字病理学任务中,FourierMIL实现了卓越的性能.
  • 在H&E染色淋巴结WSIs上的转移检测中表现出强度.
  • 在不同数据集上的肺癌分类和阿尔茨海默病病理识别中表现出有效性.

结论:

  • 福利埃米尔为数字病理学提供了一种多功能和强大的解决方案.
  • 该框架有效地处理大规模的WSIs,克服传统方法的局限性.
  • 福利埃MIL的无注意力方法为各种病理学应用提供了一个可扩展的替代方案.