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SYSTEM AND METHOD FOR A SPATIO-TEMPORAL NEURAL NETWORK FOR NON- MODULAR HIGH CONTENT PATHOLOGICAL SCREENING

机译:时空神经网络的非模块化高内涵病理筛查的系统和方法

摘要

Abstract The present invention is a system and method for a spatio-temporal neural The present invention is a system and method for a spatio-temporal neural network for non-modular high content pathological screening. The method includes following steps: obtaining images from various sources, annotating the images by medical expert through user interface, extracting features from the annotated images by a feature extractor, classifying lumen area on each of the annotated images, re-authenticating the classified lumen through user interface, exporting images in requisite format and training online and offline by user without user intervention. The annotating of images is manual The method also includes steps of data collection and domain expert support, de-noising of the noise that arises from straining process, morphological feature extraction and template based learning for classification and synthesis of images. The system for a spatio-temporal neural network for non-modular high content pathological screening includes, a high content screening (HCS) device, an annotating device, a feature extractor and a user interface. The HCS device provides data required for screening. The annotating device is used for annotation of the data by the medical expert practitioners by using a user interface to formulate ground truth and the feature extractor is used for providing stage I optics. The user interface is a display with server.
机译:摘要本发明是用于时空神经的系统和方法。本发明是用于时空神经网络的用于非模块化高含量病理学筛查的系统和方法。该方法包括以下步骤:从各种来源获得图像;医学专家通过用户界面对图像进行注释;特征提取器从注释图像中提取特征;对每个注释图像上的管腔区域进行分类;通过重新认证分类的管腔。用户界面,以必需的格式导出图像并由用户在线和离线进行培训,而无需用户干预。图像注释是手动的。该方法还包括以下步骤:数据收集和领域专家支持,对因拉伸过程而产生的噪声进行去噪,形态特征提取以及基于模板的学习以对图像进行分类和合成。用于非模块化高含量病理筛查的时空神经网络系统包括:高含量筛查(HCS)设备,注释设备,特征提取器和用户界面。 HCS设备提供了筛选所需的数据。注释设备用于医学专家从业人员通过使用用户界面来制定地面真实情况来对数据进行注释,而特征提取器用于提供第I阶段光学器件。用户界面是带有服务器的显示器。

著录项

  • 公开/公告号IN2014MU03540A

    专利类型

  • 公开/公告日2016-05-13

    原文格式PDF

  • 申请/专利权人

    申请/专利号IN3540/MUM/2014

  • 申请日2014-11-11

  • 分类号A61B5/55;

  • 国家 IN

  • 入库时间 2022-08-21 14:25:20

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