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    METHOD AND DEVICE FOR CASE-BASED DECISION SUPPORT
    2.
    发明申请
    METHOD AND DEVICE FOR CASE-BASED DECISION SUPPORT 有权
    基于案例决策支持的方法和设备

    公开(公告)号：US20100281037A1

    公开(公告)日：2010-11-04

    申请号：US12747535

    申请日：2008-12-11

    申请人： Lalitha Agnihotri ,  Lilla Boroczky ,  Luyin Zhao ,  Michael Chun-chieh Lee

    发明人： Lalitha Agnihotri ,  Lilla Boroczky ,  Luyin Zhao ,  Michael Chun-chieh Lee

    IPC分类号： G06F17/30

    CPC分类号： G06F19/345 ,  G06F19/00 ,  G16H50/20

    摘要： This invention relates to a method and device for case-based decision support. It proposes that a case-based decision support system is trained on inputs from several radiologists in order to have a “baseline” system, and then the system provides an option to a radiologist to refine the baseline system based on his/her inputs which either refine weights of features for similarity distance computation directly or provide new similarity ground truth clusters. By enabling modifying the similarity distance computation based on user inputs, this invention adapts similarity ground truth to different users with different experience and/or different opinions.

    摘要翻译： 本发明涉及用于基于病例的决策支持的方法和装置。 它建议对基于病例的决策支持系统进行培训，对来自多名放射科医师的投入进行培训，以便建立一个“基线”系统，然后该系统为放射科医师提供一个选择，以便根据他/她的输入来完善基线系统， 优化直接相似度距离计算的特征权重或提供新的相似性地面真值聚类。 通过使用基于用户输入的相似距离计算能够进行修改，本发明通过不同的经验和/或不同的观点，将不同用户的相似性原理进行了调整。

    METHOD AND APPARATUS FOR REFINING SIMILAR CASE SEARCH
    6.
    发明申请
    METHOD AND APPARATUS FOR REFINING SIMILAR CASE SEARCH 审中-公开
    改进类似案例搜索的方法和装置

    公开(公告)号：US20110022622A1

    公开(公告)日：2011-01-27

    申请号：US12747615

    申请日：2008-12-10

    申请人： Lilla Boroczky ,  Lalitha Agnihotri ,  Luyin Zhao ,  Michael Chun-chieh Lee

    发明人： Lilla Boroczky ,  Lalitha Agnihotri ,  Luyin Zhao ,  Michael Chun-chieh Lee

    IPC分类号： G06F17/30

    CPC分类号： G16H50/70

    摘要： The invention relates to search for cases in a database. According to the proposed method and apparatus, similarity matching is performed between an input case and a set of cases in an initial search to receive similar cases by—using a given matching criterion. Then statistics on image and non-image-based features associated with the similar cases are calculated and presented to the user with the similar cases. In a search refinement the similar cases are refined by additional features that are determined by the user based on the statistics. The search refinement can be iterative depending on the user's need.

    摘要翻译： 本发明涉及在数据库中搜索案例。 根据所提出的方法和装置，在初始搜索中在输入情况和一组情况之间执行相似性匹配，以通过使用给定的匹配标准来接收类似的情况。 然后，与类似情况相关联的图像和非图像特征的统计量被计算并呈现给具有类似情况的用户。 在搜索细化中，类似的情况通过基于统计信息由用户确定的附加特征进行细化。 搜索细化可以根据用户的需要进行迭代。

    METHOD AND SYSTEM FOR CROSS-MODALITY CASE-BASED COMPUTER-AIDED DIAGNOSIS
    7.
    发明申请
    METHOD AND SYSTEM FOR CROSS-MODALITY CASE-BASED COMPUTER-AIDED DIAGNOSIS 审中-公开
    基于案例的计算机辅助诊断方法与系统

    公开(公告)号：US20100272338A1

    公开(公告)日：2010-10-28

    申请号：US12747602

    申请日：2008-12-09

    申请人： Lalitha Agnihotri ,  Lilla Boroczky ,  Luyin Zhao

    发明人： Lalitha Agnihotri ,  Lilla Boroczky ,  Luyin Zhao

    IPC分类号： G06K9/00

    CPC分类号： G06F19/321 ,  G06F19/00 ,  G16H50/20 ,  G16H50/70

    摘要： A system and method for cross-modality case-based computer-aided diagnosis comprises storing a plurality of cases, each case including at least one image of one of a plurality of modalities and non-image information, mapping a feature relationship between a feature from images of a first modality to a feature from images of a second modality, and storing the relationship.

    摘要翻译： 一种用于交叉形式基于案例的计算机辅助诊断的系统和方法包括存储多个情况，每个情况包括多个模态和非图像信息中的一个的至少一个图像，其映射特征之间的特征关系 从第二模态的图像到特征的第一模态的图像，以及存储该关系。

    CLINICIAN-DRIVEN EXAMPLE-BASED COMPUTER-AIDED DIAGNOSIS
    8.
    发明申请
    CLINICIAN-DRIVEN EXAMPLE-BASED COMPUTER-AIDED DIAGNOSIS 有权
    基于实例的计算机辅助诊断

    公开(公告)号：US20090138432A1

    公开(公告)日：2009-05-28

    申请号：US12305089

    申请日：2007-06-15

    申请人： Lalitha Agnihotri ,  Lilla Boroczky ,  Luyin Zhao

    发明人： Lalitha Agnihotri ,  Lilla Boroczky ,  Luyin Zhao

    IPC分类号： G06F17/30

    CPC分类号： G16H50/20 ,  G06F19/00 ,  G06F19/321

    摘要： Optimizing example-based computer-aided diagnosis (CADx) is accomplished by clustering volumes-of-interest (VOIs) (116) in a database (120) into respective clusters according to subjective assessment of similarity (S220). An optimal set of volume-of-interest (VOI) features is then selected for fetching examples such that objective assessment of similarity, based on the selected features, clusters, in a feature space, the database VOIs so as to conform to the subjectively-based clustering (S230). The fetched examples are displayed alongside the VOI to be diagnosed for comparison by the clinician. Preferably, the displayed example is user-selectable for further display of prognosis, therapy information, follow up information, current status, and/or clinical information retrieved from an electronic medical record (S260).

    摘要翻译： 基于实例的计算机辅助诊断（CADx）通过根据相似性的主观评估将数据库（120）中的感兴趣体积（116）聚类到相应的簇中来实现（S220）。 然后选择最佳的感兴趣体积（VOI）特征来提取示例，使得基于特征空间的所选择的特征聚类的客观评估，数据库VOI以符合主观 - （S230）。 所获取的示例显示在VOI旁边以被临床医生进行诊断以进行比较。 优选地，所显示的示例是用户可选择的，用于进一步显示从电子医疗记录检索的预后，治疗信息，跟踪信息，当前状态和/或临床信息（S260）。

    False positive reduction in computer-assisted detection (CAD) with new 3D features
    9.
    发明授权
    False positive reduction in computer-assisted detection (CAD) with new 3D features 有权
    具有新3D特征的计算机辅助检测（CAD）的虚假积极减少

    公开(公告)号：US07840062B2

    公开(公告)日：2010-11-23

    申请号：US11719634

    申请日：2005-11-21

    申请人： Lilla Boroczky ,  Kwok Pun Lee ,  Luyin Zhao

    发明人： Lilla Boroczky ,  Kwok Pun Lee ,  Luyin Zhao

    IPC分类号： G06K9/00 ,  G01N23/04

    CPC分类号： G06T7/0012 ,  G06K9/3233 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30061

    摘要： A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, which subset is used to train the support vector machine to classify candidate region/volumes found within non-training data.

    摘要翻译： 在HRCT医学图像数据中检测到的计算机辅助检测（CAD）和感兴趣区域的分类方法包括后CAD机器学习技术，其应用于将区域/体积的识别的特异性和敏感性最大化为结节或非结节。 区域由CAD过程识别，并自动分段。 从每个分段区域识别和提取特征池，并通过遗传算法进行处理以识别最优特征子集，该子集用于训练支持向量机以对在非训练数据内发现的候选区域/体积进行分类。