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1. 发明申请

US20100121178A1 Systems and Methods for Automated Diagnosis and Decision Support for Breast Imaging 审中-公开
标题翻译：乳腺成像自动诊断和决策支持的系统和方法
公开(公告)号：US20100121178A1
公开(公告)日：2010-05-13
申请号：US12621363
申请日：2009-11-18
申请人： Sriram Krishnan , R. Bharat Rao , Murat Dundar , Glenn Fung
发明人： Sriram Krishnan , R. Bharat Rao , Murat Dundar , Glenn Fung
IPC分类号： A61B5/055 , A61B8/14
CPC分类号： G06T7/0012 , G06F19/321 , G06T2207/10088 , G06T2207/10116 , G06T2207/20084 , G06T2207/30068 , G16H40/63 , G16H50/20 , Y10S128/92 , Y10S128/922 , Y10S128/923 , Y10S128/924
摘要： CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.
摘要翻译：提供了用于乳腺成像的CAD（计算机辅助诊断）系统和应用，其实现了从受试患者的患者信息（包括图像数据和/或非图像数据）的集合中自动提取和分析特征的方法，以提供对医生工作流程的各个方面的决策支持，包括例如乳腺癌的自动诊断其他自动化决策支持功能，其能够为乳腺癌的筛选和分期提供决策支持。 CAD系统实施机器学习技术，其使用从一个或多个相关临床领域的标记的患者病例的数据库获得（学习）的一组训练数据和/或对这些数据的专家解释，使得CAD系统能够“学习” 分析患者数据，进行适当的诊断评估和决策，以协助医师的工作流程。

2. 发明授权

US07640051B2 Systems and methods for automated diagnosis and decision support for breast imaging 有权
标题翻译：用于乳腺成像自动诊断和决策支持的系统和方法
公开(公告)号：US07640051B2
公开(公告)日：2009-12-29
申请号：US10877129
申请日：2004-06-25
申请人： Sriram Krishnan , R. Bharat Rao , Murat Dundar , Glenn Fung
发明人： Sriram Krishnan , R. Bharat Rao , Murat Dundar , Glenn Fung
IPC分类号： A61B5/00
CPC分类号： G06T7/0012 , G06F19/00 , G06F19/321 , G06T2207/10088 , G06T2207/10116 , G06T2207/20084 , G06T2207/30068 , G16H40/63 , G16H50/20 , Y10S128/92 , Y10S128/922 , Y10S128/923 , Y10S128/924
摘要： CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.
摘要翻译：提供了用于乳腺成像的CAD（计算机辅助诊断）系统和应用，其实现了从受试患者的患者信息（包括图像数据和/或非图像数据）的集合中自动提取和分析特征的方法，以提供对医生工作流程的各个方面的决策支持，包括例如乳腺癌的自动诊断其他自动化决策支持功能，其能够为乳腺癌的筛选和分期提供决策支持。 CAD系统实施机器学习技术，其使用从一个或多个相关临床领域的标记的患者病例的数据库获得（学习）的一组训练数据和/或对这些数据的专家解释，使得CAD系统能够“学习” 分析患者数据，进行适当的诊断评估和决策，以协助医师的工作流程。

3. 发明授权

US07986827B2 System and method for multiple instance learning for computer aided detection 有权
标题翻译：用于计算机辅助检测的多实例学习的系统和方法
公开(公告)号：US07986827B2
公开(公告)日：2011-07-26
申请号：US11671777
申请日：2007-02-06
申请人： R. Bharat Rao , Murat Dundar , Balaji Krishnapuram , Glenn Fung
发明人： R. Bharat Rao , Murat Dundar , Balaji Krishnapuram , Glenn Fung
IPC分类号： G06K9/62 , G06K9/00 , G06E1/00
CPC分类号： G06K9/6277 , G06T7/0012 , G06T2207/30004
摘要： A method of training a classifier for computer aided detection of digitized medical image, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, where each region-of-interest has been labeled as either malignant or healthy. The training uses candidates that are spatially adjacent to each other, modeled by a “bag”, rather than each candidate by itself. A classifier is trained on the plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the label of the associated region-of-interest, rather than a large set of discrete constraints where at least one instance in each bag has to be correctly classified.
摘要翻译：训练用于数字化医学图像的计算机辅助检测的分类器的方法包括提供多个袋，每个袋包含在医学图像中的单个感兴趣区域的多个特征样本，其中每个感兴趣的区域已被标记为恶性或健康。培训使用空间上相邻的候选人，由“包”建模，而不是每个候选人本身。在多个特征样本袋上训练分类器，受限于根据相关区域的标签对每个袋子的凸包中的至少一个点（对应于特征样本）进行正确分类，而不是大量离散约束，每个行李中的至少一个实例必须被正确分类。

4. 发明授权

US07386165B2 System and method for a sparse kernel expansion for a Bayes classifier 失效
标题翻译：用于Bayes分类器的稀疏内核扩展的系统和方法
公开(公告)号：US07386165B2
公开(公告)日：2008-06-10
申请号：US11049187
申请日：2005-02-02
申请人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Bharat Rao
发明人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Bharat Rao
IPC分类号： G06K9/62 , G06K9/00 , G06E1/00 , G06N3/02
CPC分类号： G06K9/6256
摘要： A method and device having instructions for analyzing input data-space by learning classifiers include choosing a candidate subset from a predetermined training data-set that is used to analyze the input data-space. Candidates are temporarily added from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set. This is followed by removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed, and selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set to determine the one or more classifiers.
摘要翻译：具有用于通过学习分类器分析输入数据空间的指令的方法和设备包括从用于分析输入数据空间的预定训练数据集中选择候选子集。将候选者从候选子集临时添加到扩展集合，以通过对添加到扩展集合的候选者的一对一错误进行预先重复的评估来为输入数据空间生成新的内核空间。之后，在执行一次性错误评估之后，删除临时添加到扩展集的候选者，并且基于临时的候选者的一次性错误选择要永久添加到扩展集的候选项添加到扩展集以确定一个或多个分类器。

5. 发明申请

US20060247544A1 Characterization of cardiac motion with spatial relationship 审中-公开
标题翻译：心脏运动与空间关系的表征
公开(公告)号：US20060247544A1
公开(公告)日：2006-11-02
申请号：US11332975
申请日：2006-01-17
申请人： Maleeha Qazi , Mustafa Kamasak , Murat Dundar , Glenn Fung , Sriram Krishnan , R. Rao
发明人： Maleeha Qazi , Mustafa Kamasak , Murat Dundar , Glenn Fung , Sriram Krishnan , R. Rao
IPC分类号： A61B5/02
CPC分类号： G06T7/0012 , A61B5/00 , A61B5/055 , A61B5/7267 , A61B6/03 , A61B6/5217 , A61B8/08 , G06F19/321 , G06K9/00335 , G06K2209/051 , G06T7/20 , G06T2207/30048 , G16H50/20 , G16H50/50
摘要： Cardiac motion is automatically characterized based on spatial relationship to health. A classifier is trained for the characterization of cardiac motion. Regional wall motion abnormality assessment may be improved by combining information from neighboring segments. The structure or relationship between different segments and associated probabilities of different spatial locations being abnormal given another segment being abnormal are used for classification.
摘要翻译：基于与健康的空间关系自动表征心脏运动。训练分类器用于表征心脏运动。可以通过组合来自相邻段的信息来改善区域壁运动异常评估。使用不同分段之间的结构或关系，以及不同空间位置的相关概率在另一段异常的情况下是异常的。

6. 发明申请

US20050049497A1 Systems and methods for automated diagnosis and decision support for breast imaging 有权
标题翻译：用于乳腺成像自动诊断和决策支持的系统和方法
公开(公告)号：US20050049497A1
公开(公告)日：2005-03-03
申请号：US10877129
申请日：2004-06-25
申请人： Sriram Krishnan , R. Rao , Murat Dundar , Glenn Fung
发明人： Sriram Krishnan , R. Rao , Murat Dundar , Glenn Fung
IPC分类号： A61B8/00 , G06F19/00 , G06T7/00
CPC分类号： G06T7/0012 , G06F19/00 , G06F19/321 , G06T2207/10088 , G06T2207/10116 , G06T2207/20084 , G06T2207/30068 , G16H40/63 , G16H50/20 , Y10S128/92 , Y10S128/922 , Y10S128/923 , Y10S128/924
摘要： CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.
摘要翻译：提供了用于乳腺成像的CAD（计算机辅助诊断）系统和应用，其实现了从受试患者的患者信息（包括图像数据和/或非图像数据）的集合中自动提取和分析特征的方法，以提供对医生工作流程的各个方面的决策支持，包括例如乳腺癌的自动诊断其他自动化决策支持功能，其能够为乳腺癌的筛选和分期提供决策支持。 CAD系统实施机器学习技术，其使用从一个或多个相关临床领域的标记的患者病例的数据库获得（学习）的一组训练数据和/或对这些数据的专家解释，使得CAD系统能够“学习” 分析患者数据，进行适当的诊断评估和决策，以协助医师的工作流程。

7. 发明授权

US08131039B2 System and method for multiple-instance learning for computer aided diagnosis 有权
标题翻译：用于计算机辅助诊断的多实例学习的系统和方法
公开(公告)号：US08131039B2
公开(公告)日：2012-03-06
申请号：US12238536
申请日：2008-09-26
申请人： Balaji Krishnapuram , Vikas C. Raykar , Murat Dundar , R. Bharat Rao
发明人： Balaji Krishnapuram , Vikas C. Raykar , Murat Dundar , R. Bharat Rao
IPC分类号： G06K9/00
CPC分类号： G06K9/6278 , G06K9/6231 , G06K2209/053
摘要： A method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of images, each image including one or more candidate regions that have been identified as suspicious by a computer aided diagnosis system. Each image has been manually annotated to identify malignant regions. Multiple instance learning is applied to train a classifier to classify suspicious regions in a new image as malignant or benign by identifying those candidate regions that overlap a same identified malignant region, grouping each candidate region that overlaps the same identified malignant region into a same bag, and maximizing a probability P = ∏ i = 1 N ⁢ p i y i ⁡ ( 1 - p i ) 1 - y i , wherein N is a number of bags, pi is a probability of bag i containing a candidate region that overlaps with an identified malignant region, and yi is a label where a value of 1 indicates malignancy and 0 otherwise.
摘要翻译：一种训练用于对数字医学图像的计算机辅助诊断中的候选区域进行分类的分类器的方法包括提供训练图像组，每个图像包括被计算机辅助诊断系统识别为可疑的一个或多个候选区域。已经手动注释每个图像以识别恶性区域。应用多实例学习来训练分类器，通过识别与相同的识别的恶性区域重叠的候选区域，将新图像中的可疑区域分类为恶性或良性，将与相同的所识别的恶性区域重叠的每个候选区域分组成相同的袋子，并且最大化概率P =Πi = 1 N piyi⁡（1-pi）1-yi，其中N是袋的数量，pi是包含与所识别的恶性区域重叠的候选区域的袋i的概率， yi是1的值，表示恶性的标签，否则为0。

8. 发明申请

US20090080731A1 System and Method for Multiple-Instance Learning for Computer Aided Diagnosis 有权
公开(公告)号：US20090080731A1
公开(公告)日：2009-03-26
申请号：US12238536
申请日：2008-09-26
申请人： Balaji Krishnapuram , Vikas C. Raykar , Murat Dundar , R. Bharat Rao
发明人： Balaji Krishnapuram , Vikas C. Raykar , Murat Dundar , R. Bharat Rao
IPC分类号： G06K9/00
CPC分类号： G06K9/6278 , G06K9/6231 , G06K2209/053
摘要： A method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of images, each image including one or more candidate regions that have been identified as suspicious by a computer aided diagnosis system. Each image has been manually annotated to identify malignant regions. Multiple instance learning is applied to train a classifier to classify suspicious regions in a new image as malignant or benign by identifying those candidate regions that overlap a same identified malignant region, grouping each candidate region that overlaps the same identified malignant region into a same bag, and maximizing a probability P = ∏ i = 1 N  p i y i  ( 1 - p i ) 1 - y i , wherein N is a number of bags, pi is a probability of bag i containing a candidate region that overlaps with an identified malignant region, and yi is a label where a value of 1 indicates malignancy and 0 otherwise.

9. 发明申请

US20060210133A1 Performance adjustments in medical decision support systems 审中-公开
标题翻译：医疗决策支持系统的绩效调整
公开(公告)号：US20060210133A1
公开(公告)日：2006-09-21
申请号：US11362238
申请日：2006-02-24
申请人： Sriram Krishnan , Murat Dundar , R. Rao
发明人： Sriram Krishnan , Murat Dundar , R. Rao
IPC分类号： G06K9/00
CPC分类号： G16H40/63
摘要： A user adjusts the performance for assisted diagnosis. The sensitivity, specificity or both are adjusted as desired by the physician or end-user. By adjusting the trade-offs, a decision support system may be optimized by each user or for each case, possibly avoiding a one-fits-all approach or requiring purchase of different CAD products.
摘要翻译：用户调整辅助诊断的性能。敏感性，特异性或两者均由医师或终端用户根据需要进行调整。通过调整权衡，决策支持系统可以由每个用户或每种情况优化，可能避免一刀切的方法或要求购买不同的CAD产品。

10. 发明申请

US20050197980A1 System and method for a sparse kernel expansion for a bayes classifier 失效
标题翻译：用于Bayes分类器的稀疏内核扩展的系统和方法
公开(公告)号：US20050197980A1
公开(公告)日：2005-09-08
申请号：US11049187
申请日：2005-02-02
申请人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Rao
发明人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Rao
IPC分类号： G06K9/62 , G06E1/00
CPC分类号： G06K9/6256
摘要： A method and device having instructions for analyzing input data-space by learning classifiers include choosing a candidate subset from a predetermined training data-set that is used to analyze the input data-space. Candidates are temporarily added from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set. This is followed by removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed, and selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set to determine the one or more classifiers.
摘要翻译：具有用于通过学习分类器分析输入数据空间的指令的方法和设备包括从用于分析输入数据空间的预定训练数据集中选择候选子集。将候选者从候选子集临时添加到扩展集合，以通过对添加到扩展集合的候选者的一对一错误进行预先重复的评估来为输入数据空间生成新的内核空间。之后，在执行一次性错误评估之后，删除临时添加到扩展集的候选者，并且基于临时的候选者的一次性错误选择要永久添加到扩展集的候选项添加到扩展集以确定一个或多个分类器。

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