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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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基于幻灯片的图形协作培训对组织病理学全幻灯片图像分析.

Jun Shi, Tong Shu, Zhiguo Jiang

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    |May 19, 2025
    PubMed
    概括

    这项研究介绍了SlideGCD,这是一个新的计算病理学管道,可以模拟幻灯片间的相关性,以改进整片图像 (WSI) 分析. SlideGCD通过利用WSIs之间的关系来提高癌症诊断,以获得更好的代表性学习.

    科学领域:

    • 计算病理学计算病理学
    • 数字病理学数字病理学
    • 机器学习在瘤学中

    背景情况:

    • 整个幻灯片图像 (WSIs) 的病理特征对于癌症诊断至关重要.
    • 目前的WSI分析往往忽略了幻灯片间的相关性,错过了癌症发展过程中的重要信息.
    • 瘤发育涉及连续的组织学,形态学和遗传变化,跨越不同阶段和患者.

    研究的目的:

    • 引入一个新的计算病理管道,SlideGCD,用于增强WSI表示学习.
    • 将幻灯片间的相关性纳入WSI分析,以提高诊断准确度.
    • 将SlideGCD适应现有的多阶段学习 (MIL) 框架.

    主要方法:

    • 提出了一个名为SlideGCD的通用WSI分析管道.
    • 将癌症发展的先前知识整合到一个端到端的工作流程中.
    • 利用基于幻灯片的图表来引导传递信息和精细化幻灯片表示.
    • 将SlideGCD调整为8个最先进的MIL框架.

    主要成果:

    • 证明了SlideGCD在4个不同任务中的有效性和稳定性:癌症亚型,分期,生存预测和基因突变预测.
    • 通过结合幻灯片间的相关性,验证了WSI分析的改进.

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  • 展示了管道与各种MIL骨干的适应性.

    • 结论:

    • 通过有效地建模幻灯片之间的关系,SlideGCD在WSI分析中取得了重大进展.
    • 该管道提高了计算病理学任务现有的MIL框架的性能.
    • 通过幻灯片间的相关性,将癌症发展知识纳入 WSI 的表征学习和诊断指导,改善了 WSI 的表征学习和诊断指导.