Detection of Differentiated vs. Undifferentiated Colonies of iPS Cells Using Random Forests Mod-eled with the Multivariate Polya Distribution
Detection of Differentiated vs. Undifferentiated Colonies of iPS Cells Using Random Forests Mod-eled with the Multivariate Polya Distribution
复制标题
使用多元 Polya 分布建模的随机森林检测 iPS 细胞的分化与未分化集落
DOI：
10.1007/978-3-319-46723-8_77
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
T. Tamaki and K. Kaneda
中科院分区：
文献类型：
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作者：
B. Raytchev;A. Masuda;M. Minakawa;K. Tanaka;T. Kurita;T. Imamura;M. Suzuki;T. Tamaki and K. Kaneda
在本文中，我们提出了一种新的方法，用于自动检测未分化与分化的iPS细胞集落，这是能够实现良好的检测精度，只使用几个训练图像。图像中的局部补丁通过纹理布局过滤器在纹理元映射上的响应来表示，并使用随机森林来学习。此外，我们提出了一种新的方法，在森林中的个别树木的叶子的信息的概率建模，基于多元波利亚分布。
In this paper we propose a novel method for automatic detection of undifferentiated vs. differentiated colonies of iPS cells, which is able to achieve excellent accuracy of detection using only a few training images. Local patches in the images are represented through the responses of texture-layout filters over texton maps and learned using Random Forests. Additionally, we propose a novel method for probabilistic modeling of the information available at the leaves of the individual trees in the forest, based on the multivariate Polya distribution.