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    科学领域:

    • 数字病理学数字病理学
    • 计算机成像成像技术
    • 机器学习用于医疗保健

    背景情况:

    • 获得高质量的注释病理学样本至关重要,但需要大量的劳动力.
    • 现有的数据增强方法与病理图像的独特特征 (如局部特异性和实例关系) 相斗争.

    研究的目的:

    • 介绍CellMix,一种用于病理学图像数据增强的新框架.
    • 通过保留和引入实例关系来解决当前方法的局限性.

    主要方法:

    • CellMix采用以分发为导向的现场混合方法,将图像划分为补丁,并在批量内进行混合.
    • 一个课程学习启发,损失驱动的战略控制关系增强适应性实例探索.
    • 该方法保留了位置实例关系,同时引入了新的关系.

    主要成果:

    • 在病理图像分类任务中,CellMix实现了最先进的 (SOTA) 性能.
    • 在七个不同的数据集中表现出卓越的结果.
    • 有效地处理与配送相关的噪音和各种困难.

    结论:

    • CellMix提供了一种创新的以实例关系为中心的方法,用于病理图像分类.
    • 这种方法在数字病理学中推进了一般数据增强技术.
    • 该框架显示了改善对组织病理图像的自动化分析的巨大潜力.