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11. 发明申请

US20090234626A1 DOSE DISTRIBUTION MODELING BY REGION FROM FUNCTIONAL IMAGING 有权
标题翻译：通过功能成像对地区进行剂量分配建模
公开(公告)号：US20090234626A1
公开(公告)日：2009-09-17
申请号：US12398487
申请日：2009-03-05
申请人： Shipeng Yu , Glenn Fung , Steven Florian Petit , Hugo J.W.L. Aerts , Claudia Offermann , Michel Oellers , Philippe Lambin , Dirk de Ruysscher , Andreas Lubbertus Aloysius Johannes Dekker , Sriram Krishnan
发明人： Shipeng Yu , Glenn Fung , Steven Florian Petit , Hugo J.W.L. Aerts , Claudia Offermann , Michel Oellers , Philippe Lambin , Dirk de Ruysscher , Andreas Lubbertus Aloysius Johannes Dekker , Sriram Krishnan
IPC分类号： G06G7/60
CPC分类号： A61N5/1031 , A61N2005/1041 , G06F19/00 , G06N99/005 , G06T11/00 , G16H50/50
摘要： Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.
摘要翻译：功能成像信息用于确定给予治疗的残留疾病的概率。功能成像信息显示肿瘤不同区域的不同特征水平。输出用于规划使用的概率和/或用于按区域自动确定剂量。使用概率，剂量可以按区域分布，使得一些区域比其他区域接受更大的剂量。通过剂量区域的这种分布更可能以相同的剂量治疗肿瘤，允许较小剂量足以治疗肿瘤，和/或允许更大剂量对正常组织的风险较小或不增加。每个患者可能具有不同的肿瘤，剂量计划可以说明个体化肿瘤。也可以使用剂量施用精度的概率，使得组合的治疗概率允许有效的剂量计划。

12. 发明申请

US20070280530A1 Using Candidates Correlation Information During Computer Aided Diagnosis 有权
标题翻译：在计算机辅助诊断期间使用候选人相关信息
公开(公告)号：US20070280530A1
公开(公告)日：2007-12-06
申请号：US11742781
申请日：2007-05-01
申请人： Glenn Fung , Balaji Krishnapuram , Volkan Vural , R. Rao
发明人： Glenn Fung , Balaji Krishnapuram , Volkan Vural , R. Rao
IPC分类号： G06K9/62
CPC分类号： G06T7/0012 , G06K9/6269 , G06K9/6278
摘要： A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.
摘要翻译：一种方法和系统将候选信息相关联并提供一些相关候选者的批次分类。可以从单个数据集中识别该批候选。候选人之间可能存在内部相关性和/或差异。候选人可以考虑内部相关性和/或差异进行分类。可以确定一批候选人的位置和描述性特征。反过来，所确定的位置和/或描述性特征可以用于提高批次内的一些或所有候选者的分类的准确性。在一个实施例中，所分析的单个数据集与患者的内部图像相关联，并且考虑候选者之间的距离。两种不同的算法可以各自同时对批次中的所有样本进行分类，一种基于概率分析，另一种基于数学规划方法。可以使用替代算法。

13. 发明申请

US20070110292A1 System and method for computer aided detection via asymmetric cascade of sparse linear classifiers 有权
标题翻译：通过稀疏线性分类器的不对称级联进行计算机辅助检测的系统和方法
公开(公告)号：US20070110292A1
公开(公告)日：2007-05-17
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. Rao
发明人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. 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（w）的线性分类器的分级级联将所述异常候选分类为真阳性或假阳性 x + b），其中x是特征向量，w是加权向量，b是模型参数，其中不同的权重用于惩罚假否定和假肯定，并且其中更复杂的特征用于分级器级联的每个连续阶段。

14. 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。

15. 发明申请

US20050286773A1 System and method for feature identification in digital images based on rule extraction 有权
标题翻译：基于规则提取的数字图像中特征识别的系统和方法
公开(公告)号：US20050286773A1
公开(公告)日：2005-12-29
申请号：US11145886
申请日：2005-06-06
申请人： Glenn Fung , Sathyakama Sandilya , R. Bharat Rao
发明人： Glenn Fung , Sathyakama Sandilya , R. Bharat Rao
IPC分类号： G06K9/62 , G06K9/00
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 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 an upper bound along each of the n-axes of the unit hypercube, inversely transforming said upper bound to obtain a new rule containing one or more feature points of said one set, and removing 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，在两组中的第二组中的特征点中的两组，变换分类平面，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变换空间中的单位超立方体具有n个轴，获得沿着单位超立方体的每个n轴的上限，逆变换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，以及移除由所述新立体包含的特征点规则来自所述一套。

16. 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。

17. 发明授权

US08812240B2 Dose distribution modeling by region from functional imaging 有权
标题翻译：功能成像区域的剂量分布建模
公开(公告)号：US08812240B2
公开(公告)日：2014-08-19
申请号：US12398487
申请日：2009-03-05
申请人： Shipeng Yu , Glenn Fung , Steven Florian Petit , Hugo J. W. L. Aerts , Claudia Offermann , Michel Oellers , Philippe Lambin , Dirk de Ruysscher , Andreas Lubbertus Aloysius Johannes Dekker , Sriram Krishnan
发明人： Shipeng Yu , Glenn Fung , Steven Florian Petit , Hugo J. W. L. Aerts , Claudia Offermann , Michel Oellers , Philippe Lambin , Dirk de Ruysscher , Andreas Lubbertus Aloysius Johannes Dekker , Sriram Krishnan
IPC分类号： G01N33/48 , G01N31/00 , G06G7/48 , G06G7/58 , A61N5/10 , G06F19/00 , G06N99/00 , G06T11/00
CPC分类号： A61N5/1031 , A61N2005/1041 , G06F19/00 , G06N99/005 , G06T11/00 , G16H50/50
摘要： Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.
摘要翻译：功能成像信息用于确定给予治疗的残留疾病的概率。功能成像信息显示肿瘤不同区域的不同特征水平。输出用于规划使用的概率和/或用于按区域自动确定剂量。使用概率，剂量可以按区域分布，使得一些区域比其他区域接受更大的剂量。通过剂量区域的这种分布更可能以相同的剂量治疗肿瘤，允许较小剂量足以治疗肿瘤，和/或允许更大剂量对正常组织的风险较小或不增加。每个患者可能具有不同的肿瘤，剂量计划可以说明个体化肿瘤。也可以使用剂量施用精度的概率，使得组合的治疗概率允许有效的剂量计划。

18. 发明申请

US20140095205A1 AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS 审中-公开
标题翻译：医疗卫生系统服务代码的自动映射
公开(公告)号：US20140095205A1
公开(公告)日：2014-04-03
申请号：US14037548
申请日：2013-09-26
申请人： Faisal Farooq , Marc Overhage , Glenn Fung , Shipeng Yu , Bharat R. Rao , Balaji Krishnapuram , Jan DeHaan
发明人： Faisal Farooq , Marc Overhage , Glenn Fung , Shipeng Yu , Bharat R. Rao , Balaji Krishnapuram , Jan DeHaan
IPC分类号： G06F19/00
CPC分类号： G16H10/60
摘要： Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.
摘要翻译：提供医疗保健中语义的自动映射。数据集具有不同的语义（例如，在一个系统中用M和F指定的Gender，在另一个系统中用1或2指定的Sex）。对于语义互操作性，根据数据本身的统计，处理器创建不同医疗保健实体的语义系统之间的语义链接（例如，Gender = Sex和/或1 = F和2 = M）。使用或不使用其他信息和/或逻辑的变量，值或变量和值的分布用于创建从一个语义系统到另一个语义系统的映射。其他变量和/或值的类似分布可能是具有相同含义的变量和/或值。

19. 发明申请

US20140095201A1 Leveraging Public Health Data for Prediction and Prevention of Adverse Events 审中-公开
标题翻译：利用公共卫生数据预测和预防不良事件
公开(公告)号：US20140095201A1
公开(公告)日：2014-04-03
申请号：US14032522
申请日：2013-09-20
申请人： Faisal Farooq , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Karen Nielsen
发明人： Faisal Farooq , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Karen Nielsen
IPC分类号： G06F19/00
CPC分类号： G16H50/30
摘要： An adverse event may be prevented by predicting the probability of a given patient to have or undergo the adverse event. The ability to predict the probability of the adverse event may be enhanced when a model is derived from public health data to categorize and propose values for medical record fields. The probability alone may prevent the adverse event by educating the patient or medical professional. The probability may be predicted at any time, such as upon entry of information for the patient, periodic analysis, or at the time of admission. The probability may be used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding adverse event. The probability may be specific to a hospital, physician group, or other medical entity, allowing prevention to focus on past adverse event causes for the given entity.
摘要翻译：可以通过预测给定患者具有或经历不良事件的可能性来防止不良事件。当模型从公共卫生数据导出以对医疗记录领域进行分类和建议值时，可以增强预测不良事件概率的能力。单靠概率可以通过教育患者或医疗专业人员来预防不良事件。可以随时预测概率，例如在输入患者信息，定期分析或入院时。概率可以用于生成工作流动作项目以降低概率，警告输出适当的指令和/或协助避免不利事件。医院，医师团体或其他医疗机构的概率可能是特定的，允许预防集中于给定实体的过去不良事件原因。

20. 发明授权

US08032308B2 Modeling lung cancer survival probability after or side-effects from therapy 有权
标题翻译：建立肺癌存活概率或治疗后副作用
公开(公告)号：US08032308B2
公开(公告)日：2011-10-04
申请号：US12399274
申请日：2009-03-06
申请人： Shipeng Yu , Glenn Fung , Cary Dehing-Oberije , Dirk de Ruysscher , Sriram Krishnan , R. Bharat Rao , Philippe Lambin
发明人： Shipeng Yu , Glenn Fung , Cary Dehing-Oberije , Dirk de Ruysscher , Sriram Krishnan , R. Bharat Rao , Philippe Lambin
IPC分类号： G01N33/48 , G01N33/50
CPC分类号： G06N99/005 , A61N5/1031 , A61N2005/1041 , G06F19/00 , G16H50/50
摘要： Modeling of prognosis of survivability, side-effect, or both is provided. For example, RILI is predicted using bullae information. The amount, volume or ratio of Bullae, even alone, may indicate the likelihood of complication, such as the likelihood of significant (e.g., stage 3) pneumonitis. As another example, RILI is predicted using uptake values of an imaging agent. Standardized uptake from a functional image (e.g., FDG uptake from a positron emission image), alone or in combination with other features, may indicate the likelihood of side-effect. In another example, survivability, such as two-year survivability, is predicted using blood biomarkers. The characteristics of a patient's blood may be measured and, alone or in combination with other features, may indicate the likelihood of survival. The modeling may be for survivability, side-effect, or both and may use one or more of the blood biomarker, uptake value, and bullae features.
摘要翻译：提供了对生存能力，副作用或两者的预后的建模。例如，使用大疱信息预测RILI。 Bullae的数量，体积或比例，甚至单独可能表明并发症的可能性，例如显着（例如阶段3）肺炎的可能性。作为另一个例子，使用成像剂的摄取值来预测RILI。来自功能图像的标准摄取（例如，来自正电子发射图像的FDG摄取）单独或与其它特征组合可以指示副作用的可能性。在另一个例子中，使用血液生物标志物来预测存活能力，例如两年生存能力。可以测量患者血液的特征，单独或与其它特征组合可能表明存活的可能性。建模可以是存活性，副作用或两者，并且可以使用一种或多种血液生物标志物，摄取值和大疱特征。

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