公开(公告)号：US09336430B2

公开(公告)日：2016-05-10

申请号：US13922184

申请日：2013-06-19

申请人： HEALTH DISCOVERY CORPORATION

发明人： Hong Zhang ,  Maher Albitar

IPC分类号： G06K9/00

CPC分类号： G06K9/00147 ,  G06K9/00127 ,  G06K9/0014

摘要： A system and method for computer-assisted karyotyping includes a processor which receives a digitized image of metaphase chromosomes for processing in an image processing module and a classifier module. The image processing module may include a segmenting function for extracting individual chromosome images, a bend correcting function for straightening images of chromosomes that are bent or curved and a feature selection function for distinguishing between chromosome bands. The classifier module, which may be one or more trained kernel-based learning machines, receives the processed image and generates a classification of the image as normal or abnormal.

摘要翻译： 用于计算机辅助核型分析的系统和方法包括接收中间染色体的数字化图像以在图像处理模块和分类器模块中进行处理的处理器。 图像处理模块可以包括用于提取单个染色体图像的分割功能，用于校正弯曲或弯曲的染色体的图像的弯曲校正功能和用于区分染色体带的特征选择功能。 可以是一个或多个训练有素的基于内核的学习机器的分类器模块接收经处理的图像，并且生成正常或异常的图像分类。

公开(公告)号：US20100318482A1

公开(公告)日：2010-12-16

申请号：US12868658

申请日：2010-08-25

申请人： Peter L. Bartlett ,  André Elisseeff ,  Bernhard Schoelkopf ,  Olivier Chapelle

发明人： Peter L. Bartlett ,  André Elisseeff ,  Bernhard Schoelkopf ,  Olivier Chapelle

IPC分类号： G06F15/18

CPC分类号： G06K9/623 ,  G06F19/24 ,  G06K9/6215 ,  G06K9/6248 ,  G06K9/6269 ,  G06N99/005 ,  G06Q20/042

摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets include an invariance transformation or noise, tangent vectors are defined to identify relationships between the invariance or noise and the training data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel, which may be based on a kernel PCA map.

摘要翻译： 使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。 在数据集包括不变性变换或噪声的情况下，定义向量以识别不变性或噪声与训练数据点之间的关系。 使用正切向量形成协方差矩阵，然后用于生成内核，其可以基于内核PCA映射。

公开(公告)号：US07788193B2

公开(公告)日：2010-08-31

申请号：US11929354

申请日：2007-10-30

申请人： Peter L. Bartlett ,  André Elisseeff ,  Bernhard Schoelkopf ,  Olivier Chapelle

发明人： Peter L. Bartlett ,  André Elisseeff ,  Bernhard Schoelkopf ,  Olivier Chapelle

IPC分类号： G06F15/18 ,  G06F17/00 ,  G06N5/00

CPC分类号： G06K9/623 ,  G06F19/24 ,  G06K9/6215 ,  G06K9/6248 ,  G06K9/6269 ,  G06N99/005 ,  G06Q20/042

摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where an invariance transformation or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel.

摘要翻译： 使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。 在数据集具有结构特征的情况下，可以利用位置内核提供数据集中的数据点之间的相似度度量。 然后组合位置内核以生成可用于分析数据集的决策函数或内核。 在存在不变变换或噪声的情况下，定义向量以识别不变性或噪声与数据点之间的关系。 使用切向矢量形成协方差矩阵，然后用于生成内核。

公开(公告)号：US20090305257A1

公开(公告)日：2009-12-10

申请号：US12242912

申请日：2008-09-30

申请人： Isabelle Guyon

发明人： Isabelle Guyon

IPC分类号： C12Q1/68 ,  C12Q1/00 ,  C12Q1/02

CPC分类号： C12Q1/6886 ,  C12Q2600/112 ,  G01N33/57434 ,  G06K9/6231

摘要： Biomarkers are identified by analyzing gene expression data using support vector machines (SVM), recursive feature elimination (RFE) and/or linear ridge regression classifiers to rank genes according to their ability to separate prostate cancer from normal tissue. Proteins expressed by identified genes are detected in patient samples to screen, predict and monitor prostate cancer.

摘要翻译： 通过使用支持向量机（SVM），递归特征消除（RFE）和/或线性脊回归分类器分析基因表达数据来鉴定生物标志物，以根据将前列腺癌与正常组织分离的能力对基因进行排序。 在患者样本中检测到由鉴定的基因表达的蛋白质以筛选，预测和监测前列腺癌。

公开(公告)号：US07624074B2

公开(公告)日：2009-11-24

申请号：US11929213

申请日：2007-10-30

申请人： Jason Aaron Edward Weston ,  Andre′ Elisseeff ,  Bernard Schoelkopf ,  Fernando Pérez-Cruz

发明人： Jason Aaron Edward Weston ,  Andre′ Elisseeff ,  Bernard Schoelkopf ,  Fernando Pérez-Cruz

IPC分类号： G06F15/18

CPC分类号： G06K9/6231 ,  G06N99/005

摘要： In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l0-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score and transductive feature selection. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.

摘要翻译： 在训练学习机之前的预处理步骤中，预处理包括使用从递归特征消除（RFE）中选出的特征选择方法来减少要处理的特征量的数量，使非零参数的数量最小化 （10-norm minimization），评估成本函数以识别与由学习集施加的约束兼容的特征的子集，不平衡相关得分和转换特征选择。 然后，特征选择之后剩余的特征用于训练学习机，用于模式分类，回归，聚类和/或新颖性检测。

公开(公告)号：US07542959B2

公开(公告)日：2009-06-02

申请号：US11842934

申请日：2007-08-21

申请人： Stephen Barnhill ,  Isabelle Guyon ,  Jason Weston

发明人： Stephen Barnhill ,  Isabelle Guyon ,  Jason Weston

IPC分类号： G06N3/08 ,  G06K9/62

CPC分类号： G06K9/623 ,  G06F19/20 ,  G06F19/24 ,  G06K9/6228 ,  G06K9/6231 ,  G06K9/6256 ,  G06K9/6269 ,  G06N99/005 ,  G06Q10/0637 ,  G06Q20/10 ,  G06Q40/06 ,  G06Q50/22 ,  G06T7/0012 ,  Y02A90/22 ,  Y02A90/26

摘要： Identification of a determinative subset of features from within a large set of features is performed by training a support vector machine to rank the features according to classifier weights, where features are removed to determine how their removal affects the value of the classifier weights. The features having the smallest weight values are removed and a new support vector machine is trained with the remaining weights. The process is repeated until a relatively small subset of features remain that is capable of accurately separating the data into different patterns or classes. The method is applied for selecting the smallest number of genes that are capable of accurately distinguishing between medical conditions such as cancer and non-cancer.

摘要翻译： 通过训练支持向量机来根据分类器权重来对特征的确定性子集进行识别，其中去除特征以确定它们的去除如何影响分类器权重的值。 去除具有最小权重值的特征，并用剩余权重训练新的支持向量机。 重复该过程直到相对较小的特征子集保持能够将数据精确地分离成不同的模式或类别。 该方法适用于选择能够准确区分癌症和非癌症等医学病症的最小数量的基因。

公开(公告)号：US08543519B2

公开(公告)日：2013-09-24

申请号：US12975306

申请日：2010-12-21

申请人： Isabelle Guyon ,  Stephen D. Barnhill

发明人： Isabelle Guyon ,  Stephen D. Barnhill

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06F19/00 ,  G06F19/321 ,  G06T7/0012 ,  G06T7/62 ,  G06T7/66 ,  G06T7/68 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/30088 ,  G16H50/20 ,  G16H50/30

摘要： A system and method are provided for diagnosing diseases or conditions from digital images taken by a remote user with a smart phone or a digital camera and transmitted to an image analysis server in communication with a distributed network. The image analysis server includes a trained learning machine for classification of the images. The user-provided image is pre-processed to extract dimensional, shape and color features then is processed using the trained learning machine to classify the image. The classification result is postprocessed to generate a risk score that is transmitted to the remote user. A database associated with the server may include referral information for geographically matching the remote user with a local physician. An optional operation includes collection of financial information to secure payment for analysis services.

摘要翻译： 提供了一种用于通过智能电话或数字照相机从远程用户拍摄的数字图像诊断疾病或病症的系统和方法，并且传送到与分布式网络通信的图像分析服务器。 图像分析服务器包括用于分类图像的训练学习机。 用户提供的图像被预处理以提取尺寸，形状和颜色特征，然后使用训练学习机处理以对图像进行分类。 分类结果进行后处理，以生成传送给远程用户的风险分数。 与服务器相关联的数据库可以包括用于将远程用户与当地医师地理上匹配的引荐信息。 可选操作包括收集财务信息以确保分析服务的支付。

公开(公告)号：US20120008838A1

公开(公告)日：2012-01-12

申请号：US12975306

申请日：2010-12-21

申请人： Isabelle Guyon ,  Stephen D. Barnhill

发明人： Isabelle Guyon ,  Stephen D. Barnhill

IPC分类号： G06K9/00

CPC分类号： G06N99/005 ,  G06F19/00 ,  G06F19/321 ,  G06T7/0012 ,  G06T7/62 ,  G06T7/66 ,  G06T7/68 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/30088 ,  G16H50/20 ,  G16H50/30

摘要： A system and method are provided for diagnosing diseases or conditions from digital images taken by a remote user with a smart phone or a digital camera and transmitted to an image analysis server in communication with a distributed network. The image analysis server includes a trained learning machine for classification of the images. The user-provided image is pre-processed to extract dimensional, shape and color features then is processed using the trained learning machine to classify the image. The classification result is postprocessed to generate a risk score that is transmitted to the remote user. A database associated with the server may include referral information for geographically matching the remote user with a local physician. An optional operation includes collection of financial information to secure payment for analysis services.

摘要翻译： 提供了一种系统和方法，用于通过智能电话或数字照相机从远程用户拍摄的数字图像中诊断疾病或病症，并传送到与分布式网络通信的图像分析服务器。 图像分析服务器包括用于分类图像的训练学习机。 用户提供的图像被预处理以提取尺寸，形状和颜色特征，然后使用训练学习机处理以对图像进行分类。 分类结果进行后处理，以生成传送给远程用户的风险分数。 与服务器相关联的数据库可以包括用于将远程用户与当地医师地理上匹配的引荐信息。 可选操作包括收集财务信息以确保分析服务的支付。

公开(公告)号：US08095483B2

公开(公告)日：2012-01-10

申请号：US12957411

申请日：2010-12-01

申请人： Jason Weston ,  Isabelle Guyon

发明人： Jason Weston ,  Isabelle Guyon

IPC分类号： G06F15/18

CPC分类号： G06F19/24 ,  G06F19/20 ,  G06K9/6231

摘要： Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes.

摘要翻译： 通过使用具有类标签的训练样本来训练支持向量机来确定特征组中的特征的确定性子集来确定每个特征的值，其中基于其特征被去除。 删除具有最小值的一个或多个特征，并且使用其余特征生成更新的内核矩阵。 重复该过程，直到保持能够将数据精确地分离成不同类别的预定数量的特征。

公开(公告)号：US07970718B2

公开(公告)日：2011-06-28

申请号：US12890705

申请日：2010-09-26

申请人： Isabelle Guyon ,  Andre Elisseeff ,  Bernhard Schoelkopf ,  Jason Aaron Edward Weston ,  Fernando Perez-Cruz

发明人： Isabelle Guyon ,  Andre Elisseeff ,  Bernhard Schoelkopf ,  Jason Aaron Edward Weston ,  Fernando Perez-Cruz

IPC分类号： G06F15/18

CPC分类号： G06F19/24 ,  G06F19/20 ,  G06K9/6231

摘要： A group of features that has been identified as “significant” in being able to separate data into classes is evaluated using a support vector machine which separates the dataset into classes one feature at a time. After separation, an extremal margin value is assigned to each feature based on the distance between the lowest feature value in the first class and the highest feature value in the second class. Separately, extremal margin values are calculated for a normal distribution within a large number of randomly drawn example sets for the two classes to determine the number of examples within the normal distribution that would have a specified extremal margin value. Using p-values calculated for the normal distribution, a desired p-value is selected. The specified extremal margin value corresponding to the selected p-value is compared to the calculated extremal margin values for the group of features. The features in the group that have a calculated extremal margin value less than the specified margin value are labeled as falsely significant.

摘要翻译： 使用支持向量机将资源分为类别的“特征”组合进行评估，该支持向量机将数据集一次分为一个特征。 分离后，基于第一类中最低特征值与第二类中最高特征值之间的距离，为每个特征分配极值边缘值。 另外，对于两个类别的大量随机绘制的示例集合中的正态分布计算极值边界值，以确定具有指定的极值边界值的正态分布内的示例的数量。 使用为正态分布计算的p值，选择所需的p值。 对应于所选择的p值的指定极值余量值与所计算的特征组的极值边际值进行比较。 计算的极值余量值小于指定余量值的组中的特征被标记为错误显着。