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Method for judging cytomembrane coloring integrity of her2 pathological image based on transfer learning

A technology of pathological images and transfer learning, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems that it is difficult to collect data marked with pathological images and cost a lot of manpower and material resources

Pending Publication Date: 2021-11-12
SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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Problems solved by technology

In real applications, it is difficult to collect a large amount of data labeled with pathological images. Even if it can be collected, it will take a lot of manpower and material resources. It will take days or even weeks to train a complex deep learning model.

Method used

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  • Method for judging cytomembrane coloring integrity of her2 pathological image based on transfer learning
  • Method for judging cytomembrane coloring integrity of her2 pathological image based on transfer learning
  • Method for judging cytomembrane coloring integrity of her2 pathological image based on transfer learning

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] The method for discriminating the integrity of cell membrane coloring of her2 pathological images based on migration learning of the present invention comprises an image preprocessing step, a feature extraction step and a classifier classification step, and the image preprocessing step sequentially performs filtering, color space conversion, and color space conversion on the input original pathological image. Extract the pathological cancer nest envelope processing containing effective information to organize the original pathological image into the required input data; the feature extraction module defines all the parameters that need to be loaded from the trained Inception-V3 model, and the parameters constitute the characteristics of the her2 pathological image Vector; the classifier classification step first uses the her2 pathological image da...

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Abstract

The invention relates to a method for judging the cytomembrane coloring integrity of a her2 pathological image based on transfer learning, which comprises the steps of firstly, through image screening, dyeing separation and membrane coloring region division, manually dividing an image data set with completely wrapped membrane dyeing and an image data set with incomplete wrapped membrane dyeing by an expert as an input data set for Inception-V3 model training; and in the feature extraction step, firstly, training an Inception-V3 model, and then further training the Inception-V3 model through transfer learning to obtain a new classification model of the neural network. According to the method for judging the cytomembrane coloring integrity of the her2 pathological image, a neural network model with a good effect can be trained by using a small amount of training data in a short time through transfer learning, the accuracy of 92% or above can be achieved for different individuals, and effective help is provided for doctors to judge the breast cancer her2 positive state.

Description

technical field [0001] The invention relates to a method for discriminating the integrity of cell membrane coloring in her2 pathological images, and more specifically, to a method for discriminating the integrity of cell membrane coloring in her2 pathological images based on migration learning. Background technique [0002] Her2 is currently recognized as an important prognostic / predictive factor for breast cancer. So far, hundreds of foreign literatures have studied the relationship between Her2 and breast cancer. Numerous literature reports have pointed out that Her2 amplification is related to poor prognosis of patients. [0003] The commonly used detection method for Her2 positive status is immunohistochemistry (IHC), which detects the expression of Her2 protein, which is divided into four staining patterns: score 0 means no staining at all or less than 10% of tumor cells have cell membrane staining; score 1+ means more than 10% of tumor cells have light / slight and incom...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06N3/045Y02A90/10
Inventor 王迪李娜葛菁郭莹丁青艳李丽君卢晶晶
Owner SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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