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1. 发明授权

US09224106B2 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：US09224106B2
公开(公告)日：2015-12-29
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 , G06K9/62 , G06K9/68 , G06T7/00 , G06K9/00 , G06K9/46
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。

2. 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。

3. 发明授权

US09060685B2 Whole tissue classifier for histology biopsy slides 有权
标题翻译：全组织分类器用于组织学活检
公开(公告)号：US09060685B2
公开(公告)日：2015-06-23
申请号：US13850694
申请日：2013-03-26
申请人： NEC Laboratories America, Inc.
发明人： Eric Cosatto , Pierre-Francois Laquerre , Christopher Malon , Hans-Peter Graf
IPC分类号： G06K9/00 , A61B5/00 , G06K9/62 , G06T7/00 , G06F19/00
CPC分类号： A61B5/0033 , G06F19/36 , G06K9/00127 , G06K9/6227 , G06K9/6256 , G06T7/0012 , G06T2207/10056 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024
摘要： Disclosed is a computer implemented method for fully automated tissue diagnosis that trains a region of interest (ROI) classifier in a supervised manner, wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, and trains a tissue classifier that uses the output of the ROI classifier. For a given tissue, the method finds ROIs, extracts feature vectors in each ROI, applies the ROI classifier to each feature vector thereby obtaining a set of probabilities, provides the probabilities to the tissue classifier and outputs a final diagnosis for the whole tissue.
摘要翻译：公开了一种用于全自动组织诊断的计算机实现方法，其以受监督的方式训练感兴趣区域（ROI）分类器，其中仅在组织水平给出标签，使用反向传播的多实例学习变体的培训和火车使用ROI分类器的输出的组织分类器。对于给定的组织，该方法找到ROI，在每个ROI中提取特征向量，将ROI分类器应用于每个特征向量，从而获得一组概率，为组织分类器提供概率并输出整个组织的最终诊断。

4. 发明申请

US20130315465A1 Whole Tissue Classifier for Histology Biopsy Slides 有权
标题翻译：全组织分类器用于组织活组织检查幻灯片
公开(公告)号：US20130315465A1
公开(公告)日：2013-11-28
申请号：US13850694
申请日：2013-03-26
申请人： NEC Laboratories America, Inc.
发明人： Eric Cosatto , Pierre-Francois Laquerre , Christopher Malon , Hans-Peter Graf
IPC分类号： A61B5/00 , G06K9/62
CPC分类号： A61B5/0033 , G06F19/36 , G06K9/00127 , G06K9/6227 , G06K9/6256 , G06T7/0012 , G06T2207/10056 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024
摘要： Disclosed is a computer implemented method for fully automated tissue diagnosis that trains a region of interest (ROI) classifier in a supervised manner, wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, and trains a tissue classifier that uses the output of the ROI classifier. For a given tissue, the method finds ROIs, extracts feature vectors in each ROI, applies the ROI classifier to each feature vector thereby obtaining a set of probabilities, provides the probabilities to the tissue classifier and outputs a final diagnosis for the whole tissue.
摘要翻译：公开了一种用于全自动组织诊断的计算机实现方法，其以受监督的方式训练感兴趣区域（ROI）分类器，其中仅在组织水平给出标签，使用反向传播的多实例学习变体的培训和火车使用ROI分类器的输出的组织分类器。对于给定的组织，该方法找到ROI，在每个ROI中提取特征向量，将ROI分类器应用于每个特征向量，从而获得一组概率，为组织分类器提供概率并输出整个组织的最终诊断。

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