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。

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。

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。

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。

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。

公开(公告)号：US20050177040A1

公开(公告)日：2005-08-11

申请号：US11050599

申请日：2005-02-03

申请人： Glenn Fung ,  Murat Dundar ,  Jinbo Bi ,  R. Rao

发明人： Glenn Fung ,  Murat Dundar ,  Jinbo Bi ,  R. Rao

IPC分类号： G06K9/62 ,  G06T7/00 ,  A61B5/05

CPC分类号： G06K9/6234 ,  G06T7/0012

摘要： A method and device with instructions for analyzing an image data-space includes creating a library of one or more kernels, wherein each kernel from the library of the kernels maps the image data-space to a first data-space using at least one mapping function; and learning a linear combination of kernels in an automatic manner to generate at least one of a classifier and a regressor which is applied to the first data-space. The linear combination of kernels is used to generate a classified image-data space to detect at least one of the candidates in the classified image-data space.

摘要翻译： 一种具有用于分析图像数据空间的指令的方法和装置包括创建一个或多个内核的库，其中来自该库的库中的每个内核使用至少一个映射函数将图像数据空间映射到第一数据空间 ; 以及以自动方式学习内核的线性组合，以生成应用于所述第一数据空间的分类器和回归器中的至少一个。 使用内核的线性组合来生成分类图像数据空间，以检测分类图像数据空间中的候选者中的至少一个。

公开(公告)号：US07756313B2

公开(公告)日：2010-07-13

申请号：US11592869

申请日：2006-11-03

申请人： Jinbo Bi ,  Senthil Periaswamy ,  Kazunori Okada ,  Toshiro Kubota ,  Glenn Fung ,  Marcos Salganicoff ,  R. Bharat Rao

发明人： Jinbo Bi ,  Senthil Periaswamy ,  Kazunori Okada ,  Toshiro Kubota ,  Glenn Fung ,  Marcos Salganicoff ,  R. Bharat Rao

IPC分类号： G06K9/00

CPC分类号： G06K9/6256 ,  G06K9/6282

摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.

摘要翻译： 一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号的线性分类器（wTx）的分级级联将所述异常候选分类为真正的肯定或假阳性 + b），其中x是特征向量，w是加权向量，b是模型参数，其中使用不同权重来惩罚假否定和假阳性，并且其中对于级联的每个连续阶段使用更复杂的特征 的分类器。

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。

公开(公告)号：US20070189602A1

公开(公告)日：2007-08-16

申请号：US11671777

申请日：2007-02-06

申请人： R. Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

发明人： R. Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

IPC分类号： G06K9/62 ,  G06K9/00

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 images, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, wherein said features include texture, shape, intensity, and contrast of said region-of-interest, wherein each region-of-interest has been labeled as either malignant or healthy, and training a classifier on said 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 labeled of the associated region-of-interest.

摘要翻译： 一种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，其中所述特征包括纹理，形状 所述感兴趣区域的强度和对比度，其中每个感兴趣区域已被标记为恶性或健康的，并且在所述多个特征样本袋上训练分类器，受限于至少一个 对应于特征样本的每个袋的凸包中的点被根据相关联的感兴趣区域的标记进行正确分类。

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。