法拉:一个协作众包平台,用于表型化和组织学区域分析
Kevin Faust1, Min Li Chen1,2, Parsa Babaei Zadeh1
1Princess Margaret Cancer Centre, 101 College Street, Toronto, ON, Canada.
Nature communications
|January 17, 2025
概括
我们开发了PHARAOH,这是一个在线平台,用于高效的组织图像注释. 该工具有助于创建用于计算病理学的定制计算机视觉模型,加速研究和临床采用.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 医学中的人工智能
背景情况:
- 深度学习自动化了组织病理学,但需要大量的专家数据集,限制了临床应用.
- 目前开发计算病理学模型的方法通常是耗时和数据密集的.
研究的目的:
- 介绍PHARAOH,一个在线协作平台,用于简化组织图像注释.
- 为了使自定义计算机视觉模型用于计算病理学的开发和共享.
- 为了克服专家注释的大型数据集的局限性,在深度学习中用于基因病理学.
主要方法:
- 使用一个弱监督,人-in-the-loop学习框架.
- 利用补丁级图像特征将组织分成形态统一的组,用于批量注释.
- 在案例子集上使用集群级标签,以高效地开发定制模型.
主要成果:
- 以最少的专家投入,展示了定制PHARAOH模型的高效开发.
- 显示模型可以指导细胞特征的量化,与临床和分子数据相关联.
- 突出了通过队列级特征分析设计可解释的疾病形态模型的能力.
结论:
- 法老简化了定制计算病理学模型的开发和共享.
- 该平台的众包兼容性确保了病理学应用程序的全面目录的可扩展性.
- 促进细胞架构特征与疾病建模的生物和结果数据的相关性.
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