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细胞比较学习:使用正常和异常细胞的宫细胞病理全幻灯片图像分类方法

Jian Qin¹, Yongjun He², Yiqin Liang³

¹School of Computer Science and Technology, Anhui University of Technology, Maanshan, China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|August 31, 2024

概括

这项研究引入了一种新的计算机辅助查宫癌的方法. 该方法模仿病理学家对正常和异常细胞进行比较,在整个幻灯片图像分析中达到病理学家级准确性.

科学领域:

数字病理学数字病理学
计算生物学计算生物学
在瘤学瘤学.

背景情况:

使用计算机辅助诊断的自动化宫癌查显示了改善可访问性和降低成本的希望.
目前对整个幻灯片图像 (WSIs) 的分类性能受到患者特定变化的限制.
病理学家通过将异常细胞与同一WSI内的正常细胞进行比较来提高查精度.

研究的目的:

开发一种新的宫细胞比较学习方法,以改善自动查.
利用病理学家的知识,在WSIs中区分正常和异常细胞.
提高计算机辅助子宫癌诊断的精度和可靠性.

主要方法:

利用两种预先训练的YOLOX模型来检测WSI中的正常和异常的宫细胞.
采用自主监督模型提取细胞特征.
集成了一个变压器编码器,用于融合单元特征并生成WSI实例嵌入.
应用基于注意力的多实例学习,用于最终分类.

主要成果:

通过提出的方法,实现了0.9319的曲线下的面积 (AUC).
证明了与专业病理学家可比的表现.
在宫癌查中显示出临床应用的重大潜力.

结论:

新的比较学习方法有效地解决了WSI分析中的患者特定变异.
这种方法成功地模仿了专家病理学家的比较分析,以提高诊断准确度.
这种方法对提高自动化宫癌查的效率和可靠性具有重大前景.

关键词:

宫细胞病理学图像.深度学习是一种深度学习.多个实例的学习是多个实例的学习.整个幻灯片图像的分类整体幻灯片图像的分类.

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