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1. 发明申请

WO2005078638A2 A SYSTEM AND METHOD FOR A SPARSE KERNEL EXPANSION FOR A BAYES CLASSIFIER 审中-公开
标题翻译：一种用于贝叶斯分类器的稀疏核展开的系统和方法
公开(公告)号：WO2005078638A2
公开(公告)日：2005-08-25
申请号：PCT/US2005/003806
申请日：2005-02-04
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , DUNDAR, Murat , FUNG, Glenn , BI, Jinbo , RAO, R. Bharat
发明人： DUNDAR, Murat , FUNG, Glenn , BI, Jinbo , RAO, R. Bharat
IPC分类号： G06K9/62
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来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。从候选子集临时将候选者添加到扩展集以通过对添加到扩展集的候选者的预留重复评估来为输入数据空间生成新的内核空间。之后，在执行留一错误评估之后，删除临时添加到扩展集的候选项，并基于候选项的临时一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

2. 发明申请

WO2006083569A1 CHARACTERIZATION OF CARDIAC MOTION WITH SPATIAL RELATIONSHIP 审中-公开
标题翻译：心理运动与空间关系的表征
公开(公告)号：WO2006083569A1
公开(公告)日：2006-08-10
申请号：PCT/US2006/001931
申请日：2006-01-18
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , QAZI, Maleeha , KAMASAK, Mustafa , DUNDAR, Murat , FUNG, Glenn , KRISHNAN, Sriram , RAO, R. Bharat
发明人： QAZI, Maleeha , KAMASAK, Mustafa , DUNDAR, Murat , FUNG, Glenn , KRISHNAN, Sriram , RAO, R. Bharat
IPC分类号： A61B5/00
CPC分类号： G06T7/0012 , A61B5/00 , A61B5/055 , A61B5/7267 , A61B6/03 , A61B6/5217 , A61B8/08 , G06F19/00 , G06F19/321 , G06K9/00335 , G06K2209/051 , G06T7/20 , G06T2207/30048 , G16H50/20 , G16H50/50
摘要： Cardiac motion is automatically characterized (28) based on spatial relationship to health. A classifier is trained for the characterization of cardiac motion. Regional wall motion abnormality assessment (28) may be improved by combining information from neighboring segments (S). The structure or relationship between different segments (S) and associated probabilities of different spatial locations being abnormal given another segment (S) being abnormal are used for classification.
摘要翻译：基于与健康的空间关系，心脏运动被自动表征（28）。训练分类器用于表征心脏运动。可以通过组合来自相邻段（S）的信息来改善局部壁运动异常评估（28）。用于分类的另一段（S）异常的不同段（S）和不同空间位置的相关概率之间的结构或关系异常。

3. 发明申请

WO2006132975A1 SYSTEM AND METHOD FOR LEARNING RANKINGS VIA CONVEX HULL SEPARATION 审中-公开
标题翻译：通过凸轮分离学习排名的系统和方法
公开(公告)号：WO2006132975A1
公开(公告)日：2006-12-14
申请号：PCT/US2006/021475
申请日：2006-06-02
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , BI, Jinbo , FUNG, Glenn , KRISHNAN, Sriram , KRISHNAPURAM, Balaji , RAO, R. Bharat , ROSALES, Romer, E.
发明人： BI, Jinbo , FUNG, Glenn , KRISHNAN, Sriram , KRISHNAPURAM, Balaji , RAO, R. Bharat , ROSALES, Romer, E.
IPC分类号： G06K9/00
CPC分类号： G06K9/6269
摘要： A method for finding a ranking function /that classifies feature points in an n- dimensional space includes providing (61) a plurality of feature points Xk derived from tissue sample regions in a digital medical image, providing (62) training data A comprising training samples A j where formula (I), providing (63) an ordering E = {( P,Q )| A P A Q } of at least some training data sets where all training samples x I ∈ A p are ranked higher than any sample x j ∈ A Q , and solving (64) a mathematical optimization program to determine the ranking function f that classifies said feature points x into sets A . For any two sets A i , A j , A i - A j , and the ranking function f satisfies inequality constraints f ( x i ) ≤ f ( x j ) for all x I ∈ conv(A i ) and x j ∈ conv (A j ) , where conv ( A ) represents the convex hull of the elements of set A .
摘要翻译：一种用于寻找在第n维空间中对特征点进行分类的分级功能的方法包括提供（61）从数字医学图像中的组织样本区域导出的多个特征点X k，提供（62）包括训练样本的训练数据A 一个

4. 发明申请

WO2007056495A1 SYSTEM AND METHOD FOR COMPUTER AIDED DETECTION VIA ASYMMETRIC CASCADE OF SPARSE LINEAR CLASSIFIERS 审中-公开
标题翻译：用于计算机辅助检测的系统和方法通过非线性分类器的不对称分类
公开(公告)号：WO2007056495A1
公开(公告)日：2007-05-18
申请号：PCT/US2006/043585
申请日：2006-11-08
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , BI, Jinbo , PERIASWAMY, Senthil , OKADA, Kazunori , KUBOTA, Toshiro , FUNG, Glenn , SALGANICOFF, Marcos , RAO, R. Bharat
发明人： BI, Jinbo , PERIASWAMY, Senthil , OKADA, Kazunori , KUBOTA, Toshiro , FUNG, Glenn , SALGANICOFF, Marcos , RAO, R. Bharat
IPC分类号： G06K9/68 , G06K9/62
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 (10) and features (11, 13, 15) of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers (12, 14, 16) of the form sign (w T x+ 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（10）和特征（11,13,15），并且使用分层次分析将所述异常候选分类为真阳性或假阳性（x，x，x + b）的线性分类器（12,14,16）的级联，其中x是特征向量，w是加权向量，b是模型参数，其中使用不同的权重来惩罚假阴性和假阳性，并且其中对于分级器级联的每个连续阶段使用更复杂的特征。

5. 发明申请

WO2006096364A2 PERFORMANCE ADJUSTMENTS IN MEDICAL DECISION SUPPORT SYSTEMS 审中-公开
标题翻译：医疗决策支持系统中的性能调整
公开(公告)号：WO2006096364A2
公开(公告)日：2006-09-14
申请号：PCT/US2006/006842
申请日：2006-02-27
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , KRISHNAN, Sriram , DUNDAR, Murat , RAO, R. Bharat
发明人： KRISHNAN, Sriram , DUNDAR, Murat , RAO, R. Bharat
IPC分类号： G06F19/00
CPC分类号： G16H40/63 , G06F19/00
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。灵敏度，特异度或两者均根据医生或最终用户的需要进行调整。通过调整权衡，决策支持系统可以由每个用户或针对每种情况进行优化，可能避免采取一劳永逸的方法或要求购买不同的CAD产品。

6. 发明申请

WO2005078631A1 HIERARCHICAL MODELING IN MEDICAL ABNORMALITY DETECTION 审中-公开
标题翻译：医学异常检测中的分层建模
公开(公告)号：WO2005078631A1
公开(公告)日：2005-08-25
申请号：PCT/US2005/004188
申请日：2005-02-09
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , KRISHNAN, Sriram , BI, Jinbo , RAO, R. Bharat
发明人： KRISHNAN, Sriram , BI, Jinbo , RAO, R. Bharat
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G06F19/00 , G16H50/50
摘要： Hierarchal modeling is used to distinguish one state (26, 28, 32, 34, 38, 40, 44, 46) or class from three or more classes. In a first stage, a normal (26) or other class is distinguished from a diseased (28) or other groups of classes. If the results of the first stage classification indicate diseased (28) or data within the groups of different classes, a subsequent stage of classification is performed. In a subsequent stage of classification, the data is classified to distinguish one or more other classes (32, 34, 38, 40, 44, 46) from the remaining classes. Using two or more stages, medical information is classified by eliminating one or more possible classes in each stage to finally identify a particular class (26, 28, 32, 34, 38, 40, 44, 46) most appropriate or probable for the data.
摘要翻译：分层建模用于将一个状态（26,28,32,34,38,40,44,46）或类与三个或更多个类别区分开。在第一阶段，正常（26）或其他类别与患病（28）或其他类别的组不同。如果第一阶段分类的结果表示患病（28）或不同类别的组内的数据，则进行后续分类阶段。在分类的后续阶段，数据被分类以区分一个或多个其他类别（32,34,38,40,44,46）与其余类别。使用两个或更多个阶段，通过消除每个阶段中的一个或多个可能的类别来分类医学信息，以最终确定最合适或可能的数据的特定类别（26,28,34,34,38,40,44,46）。

7. 发明申请

WO2008156716A1 AUTOMATED REDUCTION OF BIOMARKERS 审中-公开
标题翻译：自动减少生物标志物
公开(公告)号：WO2008156716A1
公开(公告)日：2008-12-24
申请号：PCT/US2008/007468
申请日：2008-06-16
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , FUNG, Glenn , SEIGNEURIC, Renaud , KRISHNAN, Sriram , RAO, R. Bharat , LAMBIN, Philippe
发明人： FUNG, Glenn , SEIGNEURIC, Renaud , KRISHNAN, Sriram , RAO, R. Bharat , LAMBIN, Philippe
IPC分类号： G06F19/00
CPC分类号： G06F19/20 , G06F19/24
摘要： A list of biomarkers indicative of patient outcome is reduced. A computer program is applied to a set of biomarkers indicative of a patient outcome (e.g., prognosis, diagnosis, or treatment result). The computer program models the set of biomarkers with a subset of the biomarkers. The subset is identified without labeling based on the patient outcome. Instead, biomarker scores (e.g., sequence score) are used to identify the subset of biomarkers.
摘要翻译：减少了指示患者结果的生物标志物的列表。将计算机程序应用于指示患者结果的一组生物标志物（例如，预后，诊断或治疗结果）。计算机程序用生物标志物的一个子集建模该组生物标志物。基于患者结果，该子集被识别而没有标记。相反，生物标志物评分（例如，序列评分）用于鉴定生物标志物的子集。

8. 发明申请

WO2009091595A2 SYSTEM AND METHOD FOR PRIVACY PRESERVING PREDICTIVE MODELS FOR LUNG CANCER SURVIVAL ANALYSIS 审中-公开
标题翻译：用于肺癌预防分析的预防性预防模型的隐私保护系统和方法
公开(公告)号：WO2009091595A2
公开(公告)日：2009-07-23
申请号：PCT/US2009/000313
申请日：2009-01-16
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , MAASTRO CLINIC , FUNG, Glenn , RAO, R. Bharat , KRISHNAN, Sriram , YU, Shipeng , DEHING-OBERIJE, Cary , LAMBIN, Phillippe , DE RUYSSCHER, Dirk
发明人： FUNG, Glenn , RAO, R. Bharat , KRISHNAN, Sriram , YU, Shipeng , DEHING-OBERIJE, Cary , LAMBIN, Phillippe , DE RUYSSCHER, Dirk
IPC分类号： G06K9/62
CPC分类号： G06K9/62 , G06F19/00 , G06F21/6254 , G06K9/6215 , G06K9/6232 , G06K9/6269 , G16H50/20 , G16H50/70
摘要： A computer-implemented method for privacy-preserving data mining to determine cancer survival rates includes providing (21) a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix A i of cancer survival data that is not publicly available, providing (22) a class matrix D i for each of the data matrices A i , providing (23) a kernel K(A i , B) by each of said plurality of entities to allow public computation of a full kernel, and computing a(24) binary classifier that incorporates said public full kernel, wherein said classifier is adapted to classify (25) a new data vector according to a sign of said classifier.
摘要翻译：用于隐私保护数据挖掘以确定癌症存活率的计算机实现方法包括提供（21）由多个实体同意的随机矩阵B，其中每个实体i拥有不是的癌症存活数据的数据矩阵A i 为每个数据矩阵A i提供（22）类矩阵D i，提供（23）由所述多个实体中的每一个实体的核K（A i，B），以允许公共计算完整内核，以及计算包含所述公共完整内核的（24）二进制分类器，其中所述分类器适于根据所述分类器的符号对（25）新的数据向量进行分类。

9. 发明申请

WO2005124665A1 SYSTEM AND METHOD FOR FEATURE IDENTIFICATION IN DIGITAL IMAGES BASED ON RULE EXTRACTION 审中-公开
标题翻译：基于规则提取的数字图像特征识别系统与方法
公开(公告)号：WO2005124665A1
公开(公告)日：2005-12-29
申请号：PCT/US2005/020067
申请日：2005-06-07
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , FUNG, Glenn , SANDILYA, Sathyakama , RAO, R. Bharat
发明人： FUNG, Glenn , SANDILYA, Sathyakama , RAO, R. Bharat
IPC分类号： G06K9/62
CPC分类号： G06K9/6253 , G06K9/626 , G06K9/6269
摘要： A method for classifying features in a digital medical image includes providing a plurality of feature points in an N-dimensional space, wherein each feature point is a member of one of two sets, determining a classifying plane that separates feature points in a first of the two sets from feature points in a second of the two sets, transforming (32) the classifying plane wherein a normal vector to said transformed classifying plane has positive coefficients and a feature domain for one or more feature points of one set is a unit hypercube in a transformed space having n axes, obtaining (33) an upper bound along each of the n-axes of the unit hypercube, inversely transforming (34) said upper bound to obtain a new rule containing one or more feature points of said one set, and removing (35) the feature points contained by said new rule from said one set
摘要翻译：一种用于对数字医学图像中的特征进行分类的方法包括在N维空间中提供多个特征点，其中每个特征点是两组中的一个的成员，确定分类平面，从两组中的第二组中的特征点组成两组，变换（32）分类平面，其中向所述变换的分类平面的法向量具有正系数，并且一组中的一个或多个特征点的特征域是单位超立方体具有n轴的变换空间，获得（33）沿着单位超立方体的每个n轴的上限，逆向变换（34）所述上限以获得包含所述一个集合的一个或多个特征点的新规则，以及从所述一组中去除（35）所述新规则所包含的特征点

10. 发明申请

WO2008150514A3 SYSTEM AND METHOD FOR MEDICAL PREDICTIVE MODELS USING LIKELIHOOD GAMBLE PRICING 审中-公开
公开(公告)号：WO2008150514A3
公开(公告)日：2008-12-11
申请号：PCT/US2008/006970
申请日：2008-06-03
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , FUNG, Glenn , GIANG, Phan Hong , STECK, Harald , RAO, R. Bharat
发明人： FUNG, Glenn , GIANG, Phan Hong , STECK, Harald , RAO, R. Bharat
IPC分类号： G06F19/00
摘要： A method for predicting survival rates of medical patients includes providing a set D of survival data for a plurality of medical patients, providing a regression model having an associated parameter vector β, providing an example x ◊ of a medical patient whose survival probability is to be classified, calculating (71) a parameter vector (i) that maximizes a log-likelihood function of β over the set of survival data, (ii), wherein the log likelihood (ii) is a strictly concave function of β and is a function of the scalar xβ, calculating a weight (72) w ◊ for example x ◊ , calculating (73) an updated parameter vector β* that maximizes a function (iii), wherein data points (y ◊ , x ◊ , w ◊ ) augment set D, calculating (74) a fair log likelihood ratio λ f from (i) and β* using (iv), and mapping (75) the fair log likelihood ratio λ f to a fair price y f ◊ , wherein said fair price is a probability that class label y ◊ for example x ◊ has a value of 1.

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