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21. 发明申请

US20140180977A1 Computationally Efficient Whole Tissue Classifier for Histology Slides 有权
标题翻译：用于组织学幻灯片的计算有效的全组织分类器
公开(公告)号：US20140180977A1
公开(公告)日：2014-06-26
申请号：US14077400
申请日：2013-11-12
申请人： NEC Laboratories America, Inc.
发明人： Eric Cosatto , Pierre-Francois Laquerre , Christopher Malon , Hans-Peter Graf , Iain Melvin
IPC分类号： G06N99/00
CPC分类号： G06N99/005 , G06K9/00147 , G06K9/4652 , G06K9/623 , G06K9/6262 , G06K9/6857 , G06T7/0012 , G06T2207/10056 , G06T2207/20081 , G06T2207/30024
摘要： Systems and methods are disclosed for classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples, including: finding a split of features into sets of increasing computational cost, assigning a computational cost to each set; training for each set of features a classifier using training examples; training for each classifier, a utility function that scores a usefulness of extracting the next feature set for a given tissue unit using the training examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score: S=U−h*C is maximized, where U is a utility function, C is a cost of acquiring the feature and h is a weighting parameter; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state.
摘要翻译：公开了用于对两个阶段的组织学组织或标本进行分类的系统和方法。在第一阶段中，该方法包括使用处理器提供离线训练，在该训练期间，基于示例对一个或多个分类器进行训练，包括：将特征分组发现成增加计算成本的集合，为每个集合分配计算成本; 训练每组功能一个分类器使用训练样例; 每个分类器的训练，一个效用函数，其使用训练示例评估为给定组织单位提取下一个特征集的有用性。在第二阶段中，该方法包括通过提取所有组织单元的第一组特征将分类器应用于未知组织样本; 决定哪个组织单元通过找到最大化分数S = U-h * C的组织单位来提取下一组特征，其中U是效用函数，C是获取特征的成本，h是加权参数; 迭代直到满足停止标准或不能计算更多的特征; 以及基于当前状态发布组织级决定。

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