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在基因病理学中的图像搜索上

H R Tizhoosh¹, Liron Pantanowitz²

¹Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.

Journal of pathology informatics

|April 22, 2024

概括

组织病理学图像搜索使用先进的技术来匹配患者,帮助诊断和预后. 本综述涵盖了研究人员有效的计算病理学方法.

科学领域:

计算病理学计算病理学
数字病理学数字病理学
医学成像分析分析医学成像分析

背景情况:

组织病理学图像对于诊断和预后至关重要.
图像分析有助于患者匹配和疾病预测.
搜索技术的进步使组织形态的量化成为可能.

研究的目的:

审查最近的发展在他的病理学图像搜索技术.
为计算病理学研究人员提供概述.
为了突出有效,快速和高效的图像搜索方法.

主要方法:

使用相似性计算进行患者图像匹配.
利用搜索技术来量化组织形态.
将新患者数据与精选的数据库进行比较.

主要成果:

隐含量化组织形态在各种主要网站的隐含量化.
促进对诊断和预后进行比较.
能够根据图像数据对新患者进行预测.

结论:

图像搜索技术在研究和临床环境中提供了巨大的潜力.
计算病理学研究人员可以从高效的图像搜索方法中受益.
未来的应用包括改进的诊断和预后能力.

关键词:

计算病理学计算病理学图像搜索图像搜索图像搜索信息检索信息检索整个幻灯片图像的图像.

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