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Method and apparatus for training and operating a neural network for detecting breast cancer

机译:用于训练和操作用于检测乳腺癌的神经网络的方法和设备

摘要

A method and apparatus for training and operating a neural network using gated data. The neural network is a mixture of experts that performs soft partitioning of a network of experts. In a specific embodiment, the technique is used to detect malignancy by analyzing skin surface potential data. In particular, the invention uses certain patient information, such as menstrual cycle information, to gate the expert output data into particular populations, i.e., the network is soft partitioned into the populations. An Expectation-Maximization (EM) routine is used to train the neural network using known patient information, known measured skin potential data and correct diagnosis for the particular training data and patient information. Once trained, the neural network parameters are used in a classifier for predicting breast cancer malignancy when given the patient information and skin potentials of other patients.
机译:一种用于使用门控数据训练和操作神经网络的方法和装置。神经网络是专家的混合物,对专家网络进行软划分。在特定的实施方案中,该技术用于通过分析皮肤表面电位数据来检测恶性肿瘤。特别地,本发明使用诸如月经周期信息之类的某些患者信息来将专家输出数据选通到特定人群中,即,网络被软划分成人群。期望最大化(EM)例程用于使用已知的患者信息,已知的测得的皮肤电势数据以及针对特定训练数据和患者信息的正确诊断来训练神经网络。一旦得到训练,当给定患者信息和其他患者的皮肤潜能时,神经网络参数就会在分类器中用于预测乳腺癌的恶性程度。

著录项

  • 公开/公告号US6208983B1

    专利类型

  • 公开/公告日2001-03-27

    原文格式PDF

  • 申请/专利权人 SARNOFF CORPORATION;

    申请/专利号US19980126341

  • 申请日1998-07-30

  • 分类号G06F151/80;

  • 国家 US

  • 入库时间 2022-08-22 01:04:47

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