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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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在整个幻灯片图像中通过密集的空间可变性增强半监督实例分割.

Jiahui Yu, Tianyu Ma, Dong Hua

    IEEE journal of biomedical and health informatics
    |July 31, 2024
    PubMed
    概括

    本研究为整个幻灯片图像 (WSI) 引入了半监督实例细分 (Semi-IS). 半IS在有限的数据上实现了近乎最先进的结果,提高了病理学细分的准确性.

    科学领域:

    • 数字病理学数字病理学
    • 计算生物学 计算生物学
    • 医学图像分析 医学图像分析

    背景情况:

    • 目前的整个幻灯片图像 (WSI) 分段集中在瘤与背景之间.
    • 在WSIs中以有限的注释对不同的瘤实例进行细分是具有挑战性和未经探索的.

    研究的目的:

    • 为WSIs正式提出和评估半监督实例细分 (半IS).
    • 为了应对从未标记的数据中学习的挑战,以改善实例细分.

    主要方法:

    • 使用对比学习开发了一种半监督实例细分 (Semi-IS) 框架.
    • 处理图像补丁作为组成的令牌,用于学习类内相似性和类间不相似性.
    • 集成的消除噪音和保存技术来完善细分实例边界.

    主要成果:

    • 在临床多实例细分任务中,Semi-IS仅使用30%的注释数据实现了近乎完全监督的最新性能.
    • 在公开的细胞病理学数据集上,证明了大约2%的细分精度的提高.
    • 验证了半IS方法在细胞病理学和细胞病理学中的有效性和通用性.

    结论:

    • 半监督实例细分 (Semi-IS) 是WSIs的一个可行和有效的方法.

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  • 半IS显著减少了在数字病理学中需要广泛的手册注释的需求.
  • 拟议的框架显示了病理图像分析中临床应用的巨大潜力.