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细胞癌亚型:从多分辨率定位学习

Mohamad Mohamad1, Francesco Ponzio2, Santa Di Cataldo2

  • 1Université Côte d'Azur, INRIA, CNRS, Sophia Antipolis, France.

Computer methods and programs in biomedicine
|November 9, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种自我监督学习 (SSL) 框架,用于使用整个组织学幻灯片图像 (WSI) 来分类细胞癌 (RCC) 亚型. 这种方法减少了对手册注释的需求,提高了在各种临床环境中诊断效率和稳定性.

关键词:
数字病理学数字病理学细胞癌是细胞癌.自主监督学习学习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
  • 人工智能在瘤学中的应用
  • 计算机成像成像技术

背景情况:

  • 细胞癌 (RCC) 的诊断往往会延迟,影响治疗. 预后因亚型而异,需要准确的分类.
  • 目前的AI诊断工具需要大量的注释数据,这限制了它们的广泛使用.
  • 整体组织学幻灯片图像 (WSIs) 提供丰富的数据,但在注释和分析方面存在挑战.

研究的目的:

  • 调查用于RCC亚型分类的自主监督学习 (SSL) 框架.
  • 为了减少对人工智能驱动的癌症诊断的大型注释数据集的依赖.
  • 增强人工智能模型对WSIs的稳定性和通用性.

主要方法:

  • 开发了一个SSL模型,模仿病理学家在WSIs上的多尺度推理.
  • 在WSIs中跨不同放大级别的集成信息.
  • 通过使用异质获取条件的外部和内部数据集验证了模型的稳定性和概括性.

主要成果:

  • 在所有验证设置中,SSL方法实现了稳定的分类性能.
  • 证明对手动数据标签的依赖性减少.
  • 尽管图像采集 (扫描仪,工作流程) 的变化,但表现出更好的稳定性.

结论:

  • 拟议的SSL框架为RCC亚型分类提供了一个可概括和注释效率高的策略.
  • SSL有效地利用多分辨率的WSI数据,克服注释限制.
  • 这种方法有可能改善人工智能辅助的癌症诊断在现实世界的临床环境.