Thyroid cancer pathological image classification method based on deep learning

A technology for pathological images and thyroid cancer, applied in the field of image processing, can solve the problems of large amounts of data, poor classification effect of thyroid cancer pathological images, loss of feature information, etc., and achieve the effect of improving sensitivity, solving insensitivity problems, and increasing penalty coefficients

Active Publication Date: 2021-02-12
XIDIAN UNIV
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Problems solved by technology

However, the above method loses a lot of feature information during feature extraction, and requires a lot of data wh

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  • Thyroid cancer pathological image classification method based on deep learning
  • Thyroid cancer pathological image classification method based on deep learning
  • Thyroid cancer pathological image classification method based on deep learning

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Embodiment Construction

[0027] The embodiments and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0028] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0029] Step 1, build a receptive field network.

[0030] 1.1) Set three convolutional feature layers and a maximum pooling layer:

[0031] The convolution kernel size of the first convolutional feature layer Conv1 is 9×9, and the step size is 2, which is used to reduce the original 128×128 size feature map to 60×60;

[0032] The convolution kernel size of the second convolution feature layer Conv2 is 5×5, and the step size is 2, which is used to reduce the feature map after the convolution operation of the first convolution feature layer Conv1 to 28×28;

[0033] The size of the convolution kernel of the third convolutional feature layer Conv3 is 5×5, and the step size is 1;

[0034] The pooling kernel size of the maximum p...

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Abstract

The invention discloses a thyroid cancer pathological image classification method based on deep learning, and mainly solves the problem of poor thyroid cancer pathological image classification effectof an existing method. According to the implementation scheme, the method comprises the following steps: reading a thyroid pathology image database, extracting low-level convolution and pooling features by a receptive field network, and fusing the features to obtain fused low-level features; extracting high-level features, namely predicted category vectors, from the fused low-level features through a capsule network; updating the category vector through a dynamic routing algorithm to obtain a final category vector, and calculating the modulus of the category vector through a compression activation function; carrying out image reconstruction on the vector with the maximum modulus value through a decoding reconstruction network; iteratively updating weights in the receptive field network andthe capsule network to complete model training; and finally, inputting a thyroid pathological image to be classified into the trained model to obtain a final classification result. The invention improves the classification accuracy of the thyroid cancer pathological images and can be used for computer-aided diagnosis.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a classification method of pathological images of thyroid cancer, which can be used for feature extraction of pathological images of thyroid cancer and classification of pathological images of thyroid cancer. Background technique [0002] In recent years, the incidence of thyroid cancer in the world has gradually increased. The incidence of thyroid cancer ranks fourth among malignant tumors among women in urban areas in my country. Since the 1990s, the incidence of thyroid cancer in my country has increased by about three Times, with an average annual increase of 5%, the harm of thyroid cancer is getting bigger and bigger. The main diagnostic methods for thyroid cancer include thyroid autoantibodies and tumor markers, computerized tomography CT, magnetic resonance imaging MRI, and pathological diagnosis of thyroid cancer cells, among which pathological diagnosis of ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/214G06F18/241G06F18/253
Inventor 韩冰李浩然王颖王平高路
Owner XIDIAN UNIV
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