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Histopathological grading method for breast cancer based on fusion of CNN and radiomics features

A technology of feature fusion and radiomics, applied in the field of histopathological grading of breast cancer, can solve the problems of inability to classify and distinguish, shorten the time of discrimination and ensure the accuracy of discrimination

Active Publication Date: 2022-04-08
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The SBR classification of breast cancer is mainly based on observing the differentiation of cancer cells in pathological sections of patients under a microscope. At present, doctors cannot directly judge the classification from conventional mammography images.

Method used

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  • Histopathological grading method for breast cancer based on fusion of CNN and radiomics features
  • Histopathological grading method for breast cancer based on fusion of CNN and radiomics features
  • Histopathological grading method for breast cancer based on fusion of CNN and radiomics features

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Embodiment 1

[0027] Such as figure 1 As shown, a method for grading breast cancer histopathology based on CNN and radiomics feature fusion of the present invention comprises the following steps:

[0028] Step S101: Extract the tumor area of ​​the mammography image, and calculate grayscale, texture and wavelet features on the extracted mammography tumor area, and extract a total of 180-dimensional radiomics feature vectors through the above calculation; the extracted mammography The target image tumor area is made into mammary tumor image samples of the same size, and the image samples are divided into training set, verification set and test set.

[0029] Step S102: For the extracted 180-dimensional radiomics feature vector, the LASSO logistic regression model is used for feature screening, and the screened radiomics feature is used for feature fusion.

[0030] Step S103: Use the pre-trained CNN model for transfer learning, train the CNN classification model, add a new fully connected laye...

Embodiment 2

[0032] Such as figure 2 As shown, another breast cancer histopathological grading method based on CNN and radiomics feature fusion of the present invention comprises the following steps:

[0033] Step S201: Extract the tumor area of ​​the mammography image, calculate the grayscale, texture and wavelet features on the extracted mammography tumor area, and extract a total of 180-dimensional radiomics feature vectors through the above calculation; the extracted mammography The target image tumor area is made into mammary tumor image samples of the same size, and the image samples are divided into training set, verification set and test set.

[0034] The step S201 includes:

[0035] Step S2011: extract the ROI from the tumor area of ​​the mammography image to obtain the ROI image, calculate 14 grayscale features, 22 texture features and 144 wavelet features of the ROI image, and extract a total of 180-dimensional radiomics feature vectors;

[0036] The grayscale features are gr...

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Abstract

The invention relates to the technical field of CNN and image classification and recognition, in particular to a histopathological grading method for breast cancer based on fusion of CNN and radiomics features. The present invention proposes to judge the histopathological grade of breast cancer in mammography images by constructing a feature-fused CNN model, using the grayscale features, texture features and wavelet features extracted from the mammography tumor area, and performing feature screening through the LASSO logistic regression model to select For features that are highly correlated with the histopathological grade of breast cancer, the high-level semantic features extracted by CNN and the selected radiomics features are fused in the newly added fully connected layer of the network, and the CNN with feature fusion is obtained by fitting. The model was used to identify the histopathological grade of breast cancer. The invention can directly analyze the mammography target image scanned by the patient to determine the histopathological grade of breast cancer of the patient, and further shorten the discrimination time while ensuring the discrimination accuracy.

Description

technical field [0001] The invention relates to the technical field of CNN and image classification and recognition, in particular to a histopathological grading method for breast cancer based on fusion of CNN and radiomics features. Background technique [0002] Breast cancer is the most common cancer among women and the second most common cause of death among women. The global incidence of breast cancer has been on the rise since the late 1970s, and many patients died of breast cancer. Mammography mammography is the first choice, the easiest and most reliable non-invasive detection method for judging breast diseases at present, and its high resolution is helpful for early detection of breast cancer. [0003] In recent years, with the development of big data and high-performance computing, CNN (Convolutional Neural Network) has achieved remarkable results in the field of computer vision, and the recognition rate in natural image classification has exceeded the human recogn...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/80G06V10/764G06V10/82G06V10/25G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V10/25G06N3/045G06F18/24
Inventor 陈健闫镔曾磊海金金乔凯徐静波高飞徐一夫谭红娜梁宁宁
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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