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Method for generating training data based on immunohistochemistry and storage device

An immunohistochemistry and training data technology, applied in the computer field, can solve the problems of low efficiency and high labor cost requirements, and achieve the effect of accurate labeling

Active Publication Date: 2021-12-07
FUZHOU MAIXIN BIOTECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Therefore, it is necessary to provide a method for generating training data based on immunohistochemistry to solve the problem that the prior art pathology AI software is based on H&E staining and morphological data training, which requires the cooperation of senior pathologists and low efficiency and labor costs. advanced technical issues

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  • Method for generating training data based on immunohistochemistry and storage device
  • Method for generating training data based on immunohistochemistry and storage device
  • Method for generating training data based on immunohistochemistry and storage device

Examples

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Embodiment 1

[0068] The core of Example 1 is: firstly, perform multiple immunohistochemical detection of specific biomarkers in specific tissue regions / cells on a slice, mark the specific regions, and then perform immunohistochemical detection of target biomarkers on the same slice Chemical detection to determine the interpretation range of target biomarkers.

[0069] The breast cancer tissue is used as an illustration below:

[0070] According to the requirements of the "2019 Chinese Breast Cancer HER2 Detection Guidelines", when interpreting the results of HER2 immunohistochemical detection, the "observation procedure" must be carried out first, that is, the entire section should be observed under a low-power microscope to judge whether the staining is satisfactory And whether there is heterogeneity of HER2 expression. Normal mammary epithelium should not show strong membrane staining. Only the coloring of invasive carcinoma is evaluated, and the coloring of carcinoma in situ cannot be...

Embodiment 2

[0088] In addition to calculating the number of PD-L1-positive tumor cells, the PD-L1 interpretation rules for gastric cancer also need to calculate the number of PD-L1-positive tumor-related immune cells including T lymphocytes, but excluding granulocytes, plasma cells and other immune cells. Therefore, the identification of normal PD-L1 positive T lymphocytes is a key factor in the development of AI software, and on the PD-L1 immunohistochemical staining section, only the H&E staining section of another section in the corresponding area is used to detect PD-L1. When labeling L1-positive T lymphocytes, the common sense, energy and other factors of pathologists can affect the accuracy of labeling. Therefore, in this embodiment, through multiple staining, the cells with T lymphocyte marker staining and PD-L1 positive staining in the same section are the PD-L1 positive T lymphocytes that need to be marked, and the results are more intuitive. And objective and accurate.

[0089]...

Embodiment 3

[0101] Prostate Lesion Tissue Immunohistochemical Double Staining Kit adopts combined monoclonal antibody and double enzyme labeling method to simultaneously detect three antigens in a prostate tissue section: AMACR / p504s, p63, CK (HMW). AMACR is highly expressed in prostate cancer, not expressed or very weakly positive in normal prostate tissue, but scattered in prostate intraepithelial neoplasia (PIN) and atypical hyperplasia (AAH) tissue. p63 and CK(HMW) mark the nucleus and cytoplasm of prostate basal cells respectively, and the combination of the two can improve the recognition ability of basal cells. Normal prostate glands have two layers of cells (basal cells and glandular epithelial cells), while prostate cancer has no or only a small amount of basal cells. In this way, various lesions can be directly observed in the same slice with three combined antibodies, which is convenient. It is beneficial to the diagnosis and differential diagnosis of prostate cancer, PIN and A...

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Abstract

The invention relates to the technical field of computers, in particular to a method for generating training data based on immunohistochemistry and storage equipment. The method for generating the training data based on immunohistochemistry comprises the following steps: performing immunohistochemistry staining on a target object through different antibodies; marking the target object according to a dyeing result; and generating training data according to the labeling result. According to the method, a pathologist does not need to carry out labeling through HE slicing and combining histomorphology, the target tissue or cell can be rapidly and conveniently labeled, and due to the fact that the tissue or cell is labeled based on the immunohistochemical technology of the gold standard, labeling is more accurate compared with labeling through manual HE slicing and combining histomorphology.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and storage device for generating training data based on immunohistochemistry. Background technique [0002] At present, pathology AI software is based on H&E staining and morphological data training, requiring senior pathologists to combine morphology with a large number of labeling work on H&E stained sections to form a data set for AI learning, which is used for the development of AI software and apply. This poses a challenge to the knowledge and energy of pathologists, especially when there are a large number of stained sections to be labeled and the number of experienced pathologists is insufficient, which will lead to the failure to complete the labeling work in time and affect the subsequent learning progress of AI software. Contents of the invention [0003] Therefore, it is necessary to provide a method for generating training data based on immunohistochemi...

Claims

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Application Information

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IPC IPC(8): G06K9/62G16B40/00
CPCG01N1/30G01N33/57415G01N33/531
Inventor 杨清海程本亮陈惠玲王小亚吴楠楠周洪辉
Owner FUZHOU MAIXIN BIOTECH CO LTD