Cell density classification method and device, electronic device and storage medium

A classification method and density technology, applied in the field of computer learning, can solve the problem of time-consuming cell counting and achieve the effect of improving the speed

Pending Publication Date: 2022-07-19
FU TAI HUA IND SHENZHEN +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the current biological cell counting method is to calculate the number of cells in the image, and calculate the density range of stem cells in the image according to the number of cells, so that the counting of cells will be time-consuming

Method used

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  • Cell density classification method and device, electronic device and storage medium
  • Cell density classification method and device, electronic device and storage medium
  • Cell density classification method and device, electronic device and storage medium

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Experimental program
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Effect test

Embodiment 3

[0071] Embodiment 3 Obtain the position information of all center points of all groups of first coding features and the density range of biological cell images represented by each center point when training a convolutional neural network model, and each group of first coding features includes a center point And corresponding to a plurality of biological cell images in a density range, the biological cell images to be tested are input into the training convolutional neural network model to encode the biological cell images to be tested, so as to obtain the second coding feature. The position information of all the center points of an encoding feature determines the center point closest to the second encoding feature, and determines the organism to be tested according to the density range of the biological cell image represented by each center point and the nearest center point The density range of the cell image. Therefore, this case uses a training convolutional neural network...

Embodiment 4

[0099] Embodiment 4 Obtain multiple training biological cell images of different densities, input the multiple training biological cell images of different densities into the convolutional neural network model one by one to obtain the training convolutional neural network model, and obtain the training convolutional neural network model. The position information of all center points of all sets of first coding features and the density range of the biological cell image represented by each center point generated during the network model, each set of first coding features includes a center point and a plurality of corresponding to a density range Biological cell image, the biological cell image to be tested is input into the training convolutional neural network model to encode the biological cell image to be tested to obtain the second encoding feature, according to all the center points of the first encoding features of all groups. The position information determines the center...

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Abstract

The invention discloses a cell density classification method, and the method comprises the steps: obtaining the position information of all central points of all groups of first coding features generated when a convolutional neural network model is trained, and the density range of a biological cell image represented by each central point, each group of first coding features comprises a central point and corresponds to a plurality of biological cell images in a density range; inputting a to-be-tested biological cell image into the training convolutional neural network model, and encoding the to-be-tested biological cell image to obtain a second encoding feature; determining a center point closest to the second coding feature according to the position information of all center points of all groups of first coding features; and determining the density range of the biological cell image to be tested according to the density range of the biological cell image represented by each central point and the nearest central point. The invention further provides a cell density classification device, an electronic device and a computer readable storage medium, and the cell counting speed can be increased.

Description

technical field [0001] The present invention relates to the technical field of computer learning, and in particular, to a cell density classification method and device, an electronic device and a computer-readable storage medium. Background technique [0002] At present, when studying biological cells, such as biological stem cells, it is often not necessary to know the exact number of stem cells in the image, but only the density range of the stem cells in the image. However, the current biological cell counting method is to count the number of cells in an image, and calculate the density range of stem cells in the image according to the number of cells, which will result in time-consuming cell counting. SUMMARY OF THE INVENTION [0003] In view of this, it is necessary to provide a cell density classification method and device, an electronic device and a computer-readable storage medium, which can improve the speed of cell counting. [0004] A first aspect of the presen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04
CPCG06T7/0012G06N3/04G06T2207/30242G06N3/045G06F18/24133G06F18/214G06T2207/20081G06T2207/20084G06T2207/30024G06V20/698G06V10/82G06V10/454G06N3/08G06V20/693
Inventor 李宛真卢志德郭锦斌
Owner FU TAI HUA IND SHENZHEN
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