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无监督的斑点增大增强了病理图像上的球体实例细分

Fan Yang¹, Qiming He¹, Yanxia Wang^2,3

¹Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.

International journal of computer assisted radiology and surgery

|June 7, 2024

概括

这项研究引入了一种无监督的染色增大方法,以改善病理图像中的球实例细分. 该技术可以提高模型在不同染料中的性能,克服传统监督方法的局限性.

科学领域:

数字病理学数字病理学
医学图像分析医学图像分析
计算病理学计算病理学

背景情况:

用于球实例细分的监督深度学习模型在看不见的污点上表现不佳,原因是污点特定的特征突出显示.
获取多斑点注释病理学数据集是劳动密集型和耗时的,限制了模型的概括性.

研究的目的:

提出一种无监督的染色增强方法,在各种染色技术中进行强大的球实例细分.
增强培训数据集的染色多样性,无需手动注释.

主要方法:

使用对比的未配对翻译 (CUT) 来转换周期性酸-Schiff (PAS),马森三色 (MT) 和周期性酸-银甲胺 (PASM) 染色.
用Swin变压器替换了Mask R-CNN骨干,以改善实例细分中的特征提取.

主要成果:

拟议的染色增强方法在PAS,PASM和MT染色的所有指标上显著优于现有的方法.
废弃实验证实了拟议方法的各个组成部分的有效性.
该模型在一个包含216个全幻灯片图像 (WSI) 的数据集上得到了验证.

结论:

无监督的斑点增大显示出在数字病理学中改善球细分的巨大潜力.
这种方法可以扩展到病理图像分析中的其他复杂的细分任务.

关键词:

淋巴细胞细分系统的细分面具R-CNN是指一个R-CNN的面具.病理学图像分析图像分析斯温变压器是什么意思无监督的斑点增大方法

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