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相关实验视频

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基于WSIs的综合性乳腺癌评估检测技术中的高级深度学习方法:系统文献综述

Qiaoyi Xu1,2, Afzan Adam1, Azizi Abdullah1

  • 1Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Diagnostics (Basel, Switzerland)
|May 14, 2025
PubMed
概括

本综述探讨了乳腺癌全幻灯片图像分析中的深度学习挑战. 它提出了一个框架,以提高准确性,效率和可解释性,以便更好地早期检测.

关键词:
乳腺癌 乳腺癌 乳腺癌深度学习是一种深度学习.检测算法 检测算法系统的文献审查 系统的文献审查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
  • 在瘤学中使用人工智能
  • 生物医学成像分析分析

背景情况:

  • 乳腺癌仍然是全球女性的主要健康问题.
  • 在整个幻灯片图像 (WSIs) 中精确检测淋巴细胞和生物标志物对于预后至关重要.
  • 在WSIs上的深度学习面临挑战:图像可变性,注释稀缺性,可解释性和计算需求.

研究的目的:

  • 系统地审查深度学习方法,以在WSIs中检测乳腺癌.
  • 引入一种新的五维评估框架来评估这些方法.
  • 为克服基于WSI的乳腺癌诊断当前挑战提供路线图.

主要方法:

  • 按照PRISMA指南进行系统的文献审查.
  • 对39项同行评审研究和20个WSI数据集 (2020-2024) 的分析.
  • 开发一个五维评估框架:准确性,稳定性,可解释性,效率和注释质量.

主要成果:

  • 确定了深度学习中的关键挑战和创新,用于WSI乳腺癌分析.
  • 拟议的框架允许对深度学习模型进行均衡评估.
  • 强调需要提高注释质量和模型可解释性.

结论:

  • 该评论提供了对WSI乳腺癌检测当前深度学习方法的全面分析.
  • 提出了一份实际的路线图,以应对持续的挑战.
  • 为优化和将WSI技术转化为临床实践提供了可行的指导.