Breast cancer histopathologic grading method based on CNN and image histological feature fusion

A technology of radiomics and feature fusion, applied in the field of breast cancer histopathology grading, can solve the problem of inability to classify and distinguish, and achieve the effect of shortening the time of distinguishing and ensuring the accuracy of distinguishing

Active Publication Date: 2018-11-27
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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  • Application Information

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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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  • Breast cancer histopathologic grading method based on CNN and image histological feature fusion
  • Breast cancer histopathologic grading method based on CNN and image histological feature fusion
  • Breast cancer histopathologic grading method based on CNN and image histological feature fusion

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Embodiment 1

[0027] like 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 layer o...

Embodiment 2

[0032] like 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 grays...

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Abstract

The invention relates to the technical field of CNN and image classification and recognition, in particular relates to a breast cancer histopathologic grading method based on CNN and image histological feature fusion. The invention provides a method for judging the histopathologic grade of the breast cancer of the molybdenum target image by constructing a feature-fused CNN model, the features withhigh correlation with the histopathologic grade of the breast cancer are selected based on the grayscale features, the texture features and the wavelet features extracted from molybdenum target tumorregion through LASSO logistic regression model for feature selection, and then the high-level semantic features extracted by CNN and the selected image histological features are fused in the newly added full-connection layer of the network, and the fused CNN model is used for recognizing the histopathologic grade of the breast cancer. The breast cancer histopathologic grade of the patient can bedirectly analyzed and judged by the mammography target image scanned by the patient, thereby ensuring the discrimination accuracy and further shortening the discrimination time.

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 Applications(China)
IPC IPC(8): G06K9/62G06K9/32G06N3/04G06N3/08
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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