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使用微弱注释的整片图像进行结肠直肠癌分类:多个实例学习优化研究

Ahmed Saeed1, Mohamed A Ismail1, Nagia M Ghanem1

  • 1Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.

Computers in biology and medicine
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的深度学习方法,用于使用弱注释全幻灯片图像 (WSIs) 来分类结直肠癌 (CRC). 这种新的方法提高了计算机辅助诊断 (CAD) 系统的性能,达到93.05%的准确性.

关键词:
大肠直肠癌 (CRC) 是一种癌症.计算病理学 (CPATH) 是一个学科.计算机辅助诊断 (CAD) 是一种计算机辅助诊断.深度学习 (DL) 是指深度学习.多个实例学习 (MIL)整个幻灯片图像 (WSI) 的一个整体.

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科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 结肠直肠癌 (CRC) 是癌症相关死亡的主要原因,早期检测对有效治疗至关重要.
  • 组织病理学图像是CRC诊断的黄金标准,但手动分析耗时.
  • 深度学习为开发用于CRC检测的自动化计算机辅助诊断 (CAD) 系统提供了潜力.

研究的目的:

  • 开发和评估基于深度学习的CAD系统用于结直肠癌分类,使用微弱注释的全息病理幻灯片图像 (WSIs).
  • 通过提出新的WSI标签预测函数来提高WSI级别分类的多个实例学习 (MIL) 算法的性能.
  • 通过先进的预处理技术创建一个计算高效的数据集表示.

主要方法:

  • 利用深度学习技术在弱注释的基因病理学WSIs上进行CRC分类.
  • 开发并集成了新的WSI标签预测功能与多个实例学习 (MIL) 算法.
  • 应用高效的预处理方法来创建一个计算效率高的数据集.
  • 进行了多项实验以优化CAD系统.

主要成果:

  • 实现了93.05%的分类准确率,比基线准确率的84.17%显著改善.
  • 证明,仅使用弱注释的WSIs的拟议方法优于依赖强度注释数据的预训练的基线结果.
  • 集成的WSI标签预测功能大大提高了WSI级别分类的性能.

结论:

  • 提出的深度学习方法显著提高了从弱注释的整个幻灯片图像对结直肠癌分类的准确性.
  • 这种方法为结直肠癌提供了一个更高效和有效的计算机辅助诊断 (CAD) 系统,其性能优于传统方法.
  • 这些发现突出了利用弱注释数据的潜力,以进行可靠的CRC检测和诊断.