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47 条记录 (耗时 0.026 秒)

    Knowledge-based interpretable predictive model for survival analysis
    23.
    发明授权
    Knowledge-based interpretable predictive model for survival analysis 有权
    基于知识的可解释预测模型进行生存分析

    公开(公告)号：US08078554B2

    公开(公告)日：2011-12-13

    申请号：US12506583

    申请日：2009-07-21

    申请人： Glenn Fung ,  Cary Dehing-Oberije ,  Andreas Lubbertus Aloysius Johannes Dekker ,  Philippe Lambin ,  Shipeng Yu ,  Kartik Jayasurya Komati

    发明人： Glenn Fung ,  Cary Dehing-Oberije ,  Andreas Lubbertus Aloysius Johannes Dekker ,  Philippe Lambin ,  Shipeng Yu ,  Kartik Jayasurya Komati

    IPC分类号： G06N5/00

    CPC分类号： G06N7/005 ,  A61N2005/1041

    摘要： Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding.

    摘要翻译： 提供基于知识的可解释预测模型。 专家知识用于通过机器对模型进行种子培训。 专家知识可以作为图表信息并入，其涉及预测变量之间的已知因果关系。 一个预测模型被训练。 在一个实施例中，通过使用变量之间的关系，该模型甚至通过一个或多个变量的缺失值来运行。 为了应用，该模型输出预测，例如两年的肺癌患者的生存可能性。 还会输出模型的图形表示。 图形表示显示用于确定预测的变量和变量之间的关系。 图形表示可由医生或其他人解释，以协助理解。

    Incorporating spatial knowledge for classification
    26.
    发明授权
    Incorporating spatial knowledge for classification 有权
    结合空间知识进行分类

    公开(公告)号：US07634120B2

    公开(公告)日：2009-12-15

    申请号：US10915076

    申请日：2004-08-10

    申请人： Arun Krishnan ,  Glenn Fung ,  Jonathan Stoeckel

    发明人： Arun Krishnan ,  Glenn Fung ,  Jonathan Stoeckel

    IPC分类号： G06K9/00

    CPC分类号： G06T7/0012 ,  G06K9/6807 ,  G06T2207/10072 ,  G06T2207/20012 ,  G06T2207/30064

    摘要： We propose using different classifiers based on the spatial location of the object. The intuitive idea behind this approach is that several classifiers may learn local concepts better than a “universal” classifier that covers the whole feature space. The use of local classifiers ensures that the objects of a particular class have a higher degree of resemblance within that particular class. The use of local classifiers also results in memory, storage and performance improvements, especially when the classifier is kernel-based. As used herein, the term “kernel-based classifier” refers to a classifier where a mapping function (i.e., the kernel) has been used to map the original training data to a higher dimensional space where the classification task may be easier.

    摘要翻译： 我们建议基于对象的空间位置使用不同的分类器。 这种方法背后的直观思想是，几个分类器可以比涵盖整个特征空间的“通用”分类器更好地学习局部概念。 使用本地分类器确保特定类的对象在该特定类中具有更高程度的相似度。 使用本地分类器也会导致内存，存储和性能改进，特别是当分类器是基于内核的时候。 如本文所使用的，术语“基于内核的分类器”是指其中已经使用映射函数（即，内核）将原始训练数据映射到更高维度空间的分类器，其中分类任务可以更容易。

    Automated Reduction of Biomarkers
    27.
    发明申请
    Automated Reduction of Biomarkers 审中-公开
    自动降低生物标志物

    公开(公告)号：US20090006055A1

    公开(公告)日：2009-01-01

    申请号：US12135313

    申请日：2008-06-09

    申请人： Glenn Fung ,  Renaud G. Seigneuric ,  Sriram Krishnan ,  R. Bharat Rao ,  Philippe Lambin

    发明人： Glenn Fung ,  Renaud G. Seigneuric ,  Sriram Krishnan ,  R. Bharat Rao ,  Philippe Lambin

    IPC分类号： G06G7/60

    CPC分类号： G16B25/00 ,  G16B40/00

    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。

    Greedy support vector machine classification for feature selection applied to the nodule detection problem
    28.
    发明申请
    Greedy support vector machine classification for feature selection applied to the nodule detection problem 审中-公开
    贪心支持向量机分类功能选择应用于结节检测问题

    公开(公告)号：US20050105794A1

    公开(公告)日：2005-05-19

    申请号：US10924136

    申请日：2004-08-23

    申请人： Glenn Fung

    发明人： Glenn Fung

    IPC分类号： G06K9/62 ,  G06K9/00

    CPC分类号： G06K9/6269 ,  G06K9/6228

    摘要： An incremental greedy method to feature selection is described. This method results in a final classifier that performs optimally and depends on only a few features. Generally, a small number of features is desired because it is often the case that the complexity of a classification method depends on the number of features. It is very well known that a large number of features may lead to overfitting on the training set, which then leads to a poor generalization performance in new and unseen data. The incremental greedy method is based on feature selection of a limited subset of features from the feature space. By providing low feature dependency, the incremental greedy method 100 requires fewer computations as compared to a feature extraction approach, such as principal component analysis.

    摘要翻译： 描述了增量贪婪方法来进行特征选择。 这种方法导致一个最终的分类器，其执行最佳并仅依赖于几个特征。 通常，需要少量特征，因为分类方法的复杂性常常取决于特征的数量。 众所周知，大量的特征可能导致训练集上的过度拟合，这导致新的和未知的数据中的泛化性能差。 增量贪心方法基于特征空间中特征选择的有限子集。 通过提供低特征依赖性，与特征提取方法（诸如主成分分析）相比，增量贪心方法100需要较少的计算。

    Systems and Methods for Automated Diagnosis and Decision Support for Breast Imaging
    29.
    发明申请
    Systems and Methods for Automated Diagnosis and Decision Support for Breast Imaging 审中-公开
    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。

    System and method for computer aided detection via asymmetric cascade of sparse linear classifiers
    30.
    发明申请
    System and method for computer aided detection via asymmetric cascade of sparse linear classifiers 有权
    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。