首页> 外国专利> CELL ANALYSIS METHOD, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS DEVICE, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS PROGRAM, AND TRAINING PROGRAM FOR DEEP LEARNING ALGORITHM

CELL ANALYSIS METHOD, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS DEVICE, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS PROGRAM, AND TRAINING PROGRAM FOR DEEP LEARNING ALGORITHM

机译:深度学习算法,细胞分析装置,深层学习算法,小区分析计划和深度学习算法培训计划的训练方法

摘要

The types of cells that cannot be determined by use of a conventional scattergram are determined. The problem is solved by a cell analysis method for analyzing cells contained in a biological sample, by using a deep learning algorithm having a neural network structure, the cell analysis method including: causing the cells to flow in a flow path; obtaining a signal strength of a signal regarding each of the individual cells passing through the flow path, and inputting, into the deep learning algorithm, numerical data corresponding to the obtained signal strength regarding each of the individual cells; and on the basis of a result outputted from the deep learning algorithm, determining, for each cell, a type of the cell for which the signal strength has been obtained.
机译:确定不能通过使用传统散射图来确定的细胞类型。 通过使用具有神经网络结构的深度学习算法,细胞分析方法,包括:使细胞在流动路径中流动流动路径中的深入学习算法,通过使用具有神经网络结构的深度学习算法来分析生物样本中包含的细胞的细胞分析方法来解决。 获得关于通过流路的每个单独小区的信号的信号强度,并将其输入到深度学习算法中,对应于关于每个单个单元的所获得的信号强度的数值数据; 并且在从深度学习算法输出的结果的基础上,对于每个小区,确定已经获得了信号强度的小区的类型。

著录项

  • 公开/公告号EP3943933A1

    专利类型

  • 公开/公告日2022-01-26

    原文格式PDF

  • 申请/专利权人 SYSMEX CORPORATION;

    申请/专利号EP20200778791

  • 申请日2020-03-17

  • 分类号G01N33/48;C12M1/34;C12Q1/04;G01N33/49;

  • 国家 EP

  • 入库时间 2022-08-24 23:31:34

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