Hybrid cell species identification method based on fine-grained recognition

Abstract

The invention particularly relates to a hybrid cell type identification method based on fine granularity identification, comprising the following steps: a fine granularity identification convolutionalneural network model and a cell image database are established in advance; the cell image database comprises a hybrid cell image; the hybrid cell image is an image including a plurality of types of cells; the hybrid cell type identification method comprises the following steps of: 1, collecting mixed cell images; 2, inputting the mixed cell image into a fine-grained recognition convolution neuralnetwork model to obtain a cell type thermogram; 3, performing threshold that mixed cell image to obtain a binary image of the cell region; 4, combined with binary image of cell region and thermogramof cell species, the cell species identification results being obtained. The invention accurately identifies cell species according to the specificity of cell morphological characteristics, and avoidsthe shortcomings of the traditional cell species identification method that takes a long time and the process is tedious. The model can learn the morphological characteristics of fine-grained cells and identify cell types through texture information, which has high recognition accuracy and robustness.

Description

technical field [0001] The invention relates to the fields of biomedical image processing and machine learning, in particular to a method for identifying mixed cell types. Background technique [0002] In biomedical experiments, cell lines are often misidentified or cross-contaminated. The use of misidentified or cross-contaminated cell lines will lead to serious consequences such as irreproducible experimental results, wrong research conclusions, and clinical cell therapy disasters. It also wastes A lot of manpower, energy, money, etc. The traditional cell line identification method uses the method of comparing the DNA information of the cell sample with the cell bank loci to determine the type of the cell line and whether it is cross-contaminated, which is costly and time-consuming. [0003] Recently, deep convolutional neural networks have achieved great success in many vision tasks. Compared with traditional machine learning methods, convolutional neural networks do no...

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Problems solved by technology

[0005] In order to overcome the defects of the prior art, the present invention provides a method for identification of mixed cell types that is easy to operate and has accurate results. Accurate identification of cell types is performed according to the specificity of cell morphological characteristics, avoiding the long time-consuming traditional cell type identification methods. , The disadvantages of cumbersome process

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