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Flow cytometer cell scatter diagram classification method based on deep learning

A technology of flow cytometry and flow cytometry, which is applied in the field of medical diagnosis and can solve problems such as inaccurate classification

Pending Publication Date: 2021-12-31
URIT MEDICAL ELECTRONICS CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a flow cytometer cell scatter diagram classification method based on deep learning to solve the problem of inaccurate classification when the flow cytometer is faced with overlapping characteristics of different types of cells

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  • Flow cytometer cell scatter diagram classification method based on deep learning
  • Flow cytometer cell scatter diagram classification method based on deep learning
  • Flow cytometer cell scatter diagram classification method based on deep learning

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Embodiment Construction

[0032] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0033] In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0034] see figure 1 , the present invention provides a method for classifying flow cytometry cell scattergrams based on deep learning, comprising the following steps:

[0035] S101. Using a flow cytometer to collect cell characteristics of multiple samples, and performing classification and data enhancement on the samples to obtain multiple labeled samples.

[0036] Specifically, the flow cytometer collect...

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Abstract

The invention relates to the technical field of medical diagnosis, and discloses a flow cytometer cell scatter diagram classification method based on deep learning. The method comprises the following steps: collecting cell characteristics of a plurality of samples by using a flow cytometer, and carrying out classification and data enhancement on the samples to obtain a plurality of label samples; inputting the input tensor in a training set into a classification model for training, and optimizing the classification model based on a loss function containing a regular term; and inputting a test set into the optimized classification model for classification calculation to obtain classification accuracy and finish classification. The problem that the flow cytometer is inaccurate in classification when the characteristics of different types of cells are overlapped is solved. The method has high generalization ability, robustness and abnormal sample processing ability, and has high classification precision for cell classification.

Description

technical field [0001] The invention relates to the technical field of medical diagnosis, in particular to a deep learning-based method for classifying cell scattergrams of flow cytometers. Background technique [0002] Flow cytometer is an important instrument for diagnosing common diseases, among which the differential count of lymphocytes, neutrophils, monocytes, eosinophils and basophils is a key performance technical indicator for white blood cell classification . When the human body is infected with certain diseases, the number and shape of different types of cells in the blood will change. Medical staff can use the cell analyzer to discover and quantify the changes in the number and shape of cells for diagnosis and treatment. [0003] The existing cell classification methods include fixed region segmentation and dynamic region segmentation using automatic classification algorithms. At present, most cell analyzers use the method of calculating cell segmentation lines ...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04
CPCG06T7/0012G06N3/04G06T2207/30024G06F18/241G06F18/214
Inventor 卢英东韦笑秦鑫龙
Owner URIT MEDICAL ELECTRONICS CO LTD
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