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ProDiv:以原型驱动的一致的伪袋划分,用于全幻灯片图像分类.

Rui Yang1, Pei Liu1, Luping Ji1

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China.

Computer methods and programs in biomedicine
|April 12, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的原型驱动的分区 (ProDiv) 方案,以改进病理图像分类中的多个实例学习 (MIL) 的伪袋创建. ProDiv通过优化整个幻灯片图像 (WSI) 的划分来提高分类性能,以便更好地诊断癌症.

关键词:
计算病理学计算病理学多个实例的学习是多个实例的学习.补丁实例实例是一个补丁实例.伪袋分公司整个幻灯片图像的分类.

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学图像分析图像分析
  • 机器学习在医疗保健中的应用

背景情况:

  • 病理图像分类对于癌症诊断至关重要.
  • 带有弱标签的全幻灯片图像 (WSI) 对传统方法构成挑战.
  • 基于伪袋的多实例学习 (MIL) 是WSI分类的一个有希望的方法.

研究的目的:

  • 为病理图像提出一种改进的方法,用于在MIL框架中分割伪袋.
  • 通过优化伪袋生成来提高现有的MIL方法的分类性能.
  • 解决随机或基于集群的伪袋分区方案的局限性.

主要方法:

  • 一个由原型驱动的分部 (ProDiv) 计划被引入用于WSI伪袋的生成.
  • 一种基于注意力的方法为每个幻灯片生成一个"袋子原型".
  • WSI补丁实例基于与原型的特征相似性进行集群,通过将不同集群的实例结合起来,形成伪袋.

主要成果:

  • 与现有的MIL方法集成的ProDiv方案在分类性能方面取得了显著的改进.
  • 在两个公开的WSI数据集上,观察到AUC改善高达7.3%和10.3%.
  • 通过经验结果和实验可视化验证了ProDiv的有效性.

结论:

  • ProDiv 计划始终提高了 MIL 模型在病理图像分类中的性能.
  • 拟议的方法为MIL框架的伪袋分区提供了一种实用和有效的方法.
  • 通过改进的WSI分析,ProDiv展示了通过改进的WSI分析来推进自动化癌症诊断的潜力.