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相关实验视频

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多尺度表示基于注意力的深度多实例学习,用于千兆像素全幻灯片图像分析.

Hangchen Xiang1, Junyi Shen2, Qingguo Yan3

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

Medical image analysis
|July 19, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了全幻灯片图像 (WSI) 分析的深度多重实例学习 (MIL) 框架,提高了瘤诊断的准确性和可解释性,而无需额外的注释.

关键词:
卷积神经网络是一种卷积神经网络.多尺度表示注意力注意力.监管能力较弱的监管机构整个幻灯片图像的图像.

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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
  • 数字病理学数字病理学

背景情况:

  • 卷积神经网络 (CNN) 越来越多地用于使用整个幻灯片图像 (WSI) 进行瘤诊断.
  • 训练CNN通常需要幻灯片级标签,但处理千兆像素WSIs由于其大小和图像内部变化而存在挑战.
  • 现有的方法难以直接分析大规模的WSIs.

研究的目的:

  • 为整个幻灯片图像 (WSI) 分析提出一种新的,端到端可解释的深度多实例学习 (MIL) 框架.
  • 为了克服与直接处理千兆像素WSIs在瘤诊断中的挑战.
  • 为了提高WSI分析中的分类准确性和模型解释性.

主要方法:

  • 提出了一个双分支深度神经网络与多尺度表示注意力机制相结合.
  • WSIs被分为袋,补丁和细胞级图像,将WSI分类作为一个MIL问题.
  • 该框架直接从每个WSI中的所有补丁,挖矿袋标签,重要补丁和细胞级信息中提取特征.

主要成果:

  • 与最先进的方法相比,拟议的框架显示出更高的性能.
  • 在WSI分析中实现了更高的分类准确性.
  • 显著改善了模型的解释性.

结论:

  • 新的深度MIL框架有效地解决了千兆像素WSI分析的挑战.
  • 该方法为准确和可解释的瘤诊断提供了一个强大的工具.
  • 该框架挖掘多层次信息的能力提高了其诊断能力.