Bone marrow cell classification method and classification device based on depth learning

A bone marrow cell, deep learning technology, applied in image analysis, understanding of medical/anatomical patterns, image data processing, etc., can solve the problems of poor reproducibility, strong subjectivity, large errors, etc. Low hardware requirements and good reproducibility

Inactive Publication Date: 2019-02-19
北京羽医甘蓝信息技术有限公司
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Problems solved by technology

[0005] In view of this, the present invention provides a bone marrow cell classification method and classification device based on deep lear

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  • Bone marrow cell classification method and classification device based on depth learning
  • Bone marrow cell classification method and classification device based on depth learning
  • Bone marrow cell classification method and classification device based on depth learning

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Embodiment Construction

[0022] Exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present invention to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

[0023] figure 2 is a schematic flowchart of a method for classifying bone marrow cells based on deep learning according to an embodiment of the present invention. Such as figure 2 As shown, the method may include the following steps S1 to S4.

[0024] S1: Labeling cell locations and classification labels of the bone marrow cells in the bone marrow cell sample image. ...

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Abstract

The invention provides a bone marrow cell classification method and classification device based on depth learning, wherein the method comprises the following steps: marking a cell position and a classification tag of bone marrow cells in an image of a bone marrow cell sample; extracting a preset size image block sample with a single classification tag from a bone marrow cell sample image; The convolution neural network of bone marrow cell classification task is constructed, and then the training set composed of image block samples is used to train, and the bone marrow cell classification modelis obtained. The bone marrow cells to be tested image is cut into a plurality of test image blocks with preset size, and the plurality of test image blocks are traversed and input into the bone marrow cells classification model, the bone marrow cells edges in the plurality of test image blocks are detected, and the classification labels corresponding to the bone marrow cells and the classification confidence probabilities are output.

Description

technical field [0001] The invention relates to the technical field of computers and software, in particular to a deep learning-based bone marrow cell classification method and classification device. Background technique [0002] Leukemia is a malignant tumor originating from the hematopoietic system, and its incidence rate ranks sixth among various tumors. Cytomorphology is the most widely used, most direct and economical important diagnostic method in the diagnosis of acute leukemia, and it is an important part of morphological, immunology, cytogenetics, and molecular biology typing diagnosis. The morphological method is mainly to perform Wright-Giemsa staining analysis on the patient's bone marrow smear and blood smear, and further perform other cytochemical staining, and determine the type of acute leukemia according to the FAB (French, American, British) standard. [0003] However, bone marrow cells include multiple lines, and each line of cells is divided into three s...

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Application Information

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IPC IPC(8): G06T7/00G06K9/00G06K9/62
CPCG06T7/0012G06T2207/30024G06T2207/20084G06T2207/20081G06T2207/10056G06V20/698G06V2201/03G06F18/2413
Inventor 徐通陈文白海龙丁鹏
Owner 北京羽医甘蓝信息技术有限公司
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