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    • 1. 发明授权
    • Computer-aided image analysis
    • 计算机辅助图像分析
    • US07383237B2
    • 2008-06-03
    • US11349542
    • 2006-02-06
    • Hong ZhangGarry CarlsStephen D. Barnhill
    • Hong ZhangGarry CarlsStephen D. Barnhill
    • G06E1/00G06E3/00G06F15/18G06G7/00
    • G06T7/0012G06F19/00G06K9/623G06K9/6269G06K9/6292G06K2209/05G06N99/005G16H30/40G16H50/70
    • Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.
    • 数字化图像数据被输入到处理器中,其中检测组件识别图像中特别感兴趣的区域(对象),并且通过分割将这些对象与背景分离。 特征提取组件从分段对象中制定与分类任务相关的数值。 将前述分析步骤的结果输入到经过训练的学习机器分类器中,所述训练学习机器分类器产生输出,该输出可以由区分两种可能的诊断的指标或者期望的输出格式的一些其他输出组成。 在一个实施例中,数字化图像数据被输入到多个子系统中,每个子系统具有一个或多个支持向量机。 预处理可以包括使用有助于提取有用数据的已知变换。 每个子系统分析与图像中发现的不同特征或特征相关的数据。 一旦每个子系统完成其分析和分类,所有子系统的输出被输入到整体支持向量机分析器中,该分析器将数据组合以进行利用从图像获得的知识的诊断,决定或其他动作。
    • 6. 发明授权
    • Enhancing knowledge discovery using multiple support vector machines
    • 使用多个支持向量机增强知识发现
    • US06427141B1
    • 2002-07-30
    • US09568301
    • 2000-05-09
    • Stephen D. Barnhill
    • Stephen D. Barnhill
    • G06F1518
    • G06K9/6256G06K9/6269G06N99/005
    • A system and method for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Pre-processing data involves transforming the data points and/or expanding the data points. By adding meaning to the data, the learning machine is provided with a greater amount of information for processing. With regard to support vector machines in particular, the greater the amount of information that is processed, the better generalizations about the derived data. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels is to be adjusted and one or more support vector machines is to be retrained and retested. When it is determined that an optimal solution has been achieved, live data is pre-processed and input into the support vector machine comprising the kernel that produced the optimal solution. The live output from the learning machine is post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process.
    • 一种用于通过使用多个学习机器的数据来增强知识发现的系统和方法,特别是多个支持向量机。 学习机器的训练数据被预先处理,以便增加其意义。 预处理数据涉及变换数据点和/或扩展数据点。 通过增加数据的含义,学习机器被提供更多的处理信息。 对于特别是支持向量机,处理的信息量越大,关于派生数据的更好的概括。 每个包含不同内核的多个支持向量机用预处理的训练数据进行训练,并用以相同方式预处理的测试数据进行测试。 比较来自多个支持向量机的测试输出,以确定哪个测试输出(如果有的话)代表最优解。 要调整一个或多个内核的选择,并对一个或多个支持向量机进行再培训和重新测试。 当确定已经达到最佳解时,实时数据被预处理并输入到产生最佳解的内核的支持向量机中。 来自学习机的实时输出被后处理成计算导出的字母数字分类器,用于人或计算机自动化过程的解释。
    • 7. 发明授权
    • Enhancing knowledge discovery using support vector machines in a
distributed network environment
    • US6157921A
    • 2000-12-05
    • US305345
    • 1999-05-01
    • Stephen D. Barnhill
    • Stephen D. Barnhill
    • G06F20060101G06F15/18G06K9/62G06F17/00
    • G06K9/6256G06K9/6269G06N99/005
    • A system and method for enhancing knowledge discovery from data using a learning machine in general and a support vector machine in particular in a distributed network environment. A customer may transmit training data, test data and live data to a vendor's server from a remote source, via a distributed network. The customer may also transmit to the server identification information such as a user name, a password and a financial account identifier. The training data, test data and live data may be stored in a storage device. Training data may then be pre-processed in order to add meaning thereto. Pre-processing data may involve transforming the data points and/or expanding the data points. By adding meaning to the data, the learning machine is provided with a greater amount of information for processing. With regard to support vector machines in particular, the greater the amount of information that is processed, the better generalizations about the data that may be derived. The learning machine is therefore trained with the pre-processed training data and is tested with test data that is pre-processed in the same manner. The test output from the learning machine is post-processed in order to determine if the knowledge discovered from the test data is desirable. Post-processing involves interpreting the test output into a format that may be compared with the test data. Live data is pre-processed and input into the trained and tested learning machine. The live output from the learning machine may then be post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process. Prior to transmitting the alpha numerical classifier to the customer via the distributed network, the server is operable to communicate with a financial institution for the purpose of receiving funds from a financial account of the customer identified by the financial account identifier.
    • 8. 发明授权
    • System and method for remote melanoma screening
    • 远程黑素瘤筛查的系统和方法
    • US08543519B2
    • 2013-09-24
    • US12975306
    • 2010-12-21
    • Isabelle GuyonStephen D. Barnhill
    • Isabelle GuyonStephen D. Barnhill
    • G06F15/18
    • G06N99/005G06F19/00G06F19/321G06T7/0012G06T7/62G06T7/66G06T7/68G06T2207/10024G06T2207/20081G06T2207/30088G16H50/20G16H50/30
    • 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.
    • 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。
    • 9. 发明申请
    • SYSTEM AND METHOD FOR REMOTE MELANOMA SCREENING
    • 用于远程MELANOMA筛选的系统和方法
    • US20120008838A1
    • 2012-01-12
    • US12975306
    • 2010-12-21
    • Isabelle GuyonStephen D. Barnhill
    • Isabelle GuyonStephen D. Barnhill
    • G06K9/00
    • G06N99/005G06F19/00G06F19/321G06T7/0012G06T7/62G06T7/66G06T7/68G06T2207/10024G06T2207/20081G06T2207/30088G16H50/20G16H50/30
    • 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.
    • 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。