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Cristian Tommasino¹, Francesco Merolla², Cristiano Russo³

¹Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio 21, Naples, 80125, Italy. cristian.tommasino@unina.it.

Journal of digital imaging

|June 9, 2023

概括

这项研究探讨了使用卷积神经网络 (CNN) 来从整个幻灯片图像 (WSI) 中提取特征,以更好地诊断癌症. 这些发现显示了计算机辅助病理信息检索系统的有希望的结果.

科学领域:

数字病理学数字病理学
计算解剖学的计算解剖学
医疗图像分析医学图像分析

背景情况:

组织学幻灯片的数字化导致整个幻灯片图像 (WSIs) 的激增.
有效的存档和检索系统对于世界卫生组织在癌症诊断和研究方面至关重要.
图片存档和通信系统 (PACS) 为管理这些数据提供了一个解决方案.

研究的目的:

在PACS.中开发一个可靠的方法来查询病理学数据.
调查基于内容的图像检索 (CBIR) 对病理图像检索的有效性.
探索从WSIs中提取特征的新方法,以提高检索准确度.

主要方法:

从全幻灯片图像 (WSI) 补丁中提取特征,使用预训练的卷积神经网络 (CNN).
评估了不同CNN层的特征,并应用了各种维度减小技术.
对检索结果进行了定性分析.

主要成果:

识别了使用CNN的WSIs的有效特征表示.
展示了不同CNN层和维度减小技术在图像检索中的潜力.
在病理学中为拟议的CBIR框架取得了令人鼓舞的结果.

结论:

拟议的框架显示了增强数字病理学信息检索的希望.
使用CNN从WSIs中提取特征是改进CBIR系统的可行方法.
这些方法的进一步开发可以显著帮助癌症诊断和研究.

关键词:

计算病理学计算病理学基于内容的图像检索.深度学习是一种深度学习.PACS PACS 是一个小组.

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