A multi-channel collaborative capsule network-based method for classifying pathological images of colon cancer
A colon cancer and pathology technology, applied in the recognition of medical/anatomical models, instruments, computing, etc., can solve problems such as no rotation, loss of spatial information, etc., achieve low cost, accelerate model training, and improve generalization capabilities. Effect
Active Publication Date: 2022-07-22
ZHEJIANG UNIV OF TECH
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The present invention effectively overcomes the problems of traditional convolutional neural network technology such as loss of spatial information, lack of rotation and translation invariance in the training process, and uses feature fusion to improve the generalization ability of the model. The capsule layer allows collaborative cooperation between channels to accelerate Training, improving the accuracy of the capsule network in colon cancer pathology classification and reducing the training time of the model
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[0079] The present invention will be described in detail below with reference to specific embodiments, but the protection scope of the present invention is not limited to the following embodiments.
[0080] like figure 1 As shown, the main structure of the system in this implementation case includes: an image data acquisition module, an image data preprocessing module, an image feature extraction module, a network training module, and a test evaluation module; the image data acquisition module is used for crawling and collecting colon cancer. Pathological images and structured data processing; image data preprocessing module includes using Min-Max Normalization to normalize data, using One-Hot Encoder to label image categories, and using data augmentation technology to enhance data robustness properties and generalization; image feature extraction module, using weight self-adjusting feature fusion technology to extract image features; network training module, using capsule netw...
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A method for classifying colon cancer pathology images based on multi-channel collaborative capsule network. The network model is constructed by using the dynamic routing mechanism of the capsule network. channel to speed up the training, and then use the margin loss loss function to train the model. The invention effectively overcomes the problems such as loss of spatial information, no rotation and translation invariance in the training process of the traditional convolutional neural network technology, and improves the generalization ability of the model by using feature fusion, and the capsule layer allows the cooperation between channels to accelerate Training can effectively save time and cost, reduce network parameters, and make network training more efficient.
Description
technical field [0001] The invention relates to the fields of deep learning, medical image processing and computer-aided treatment, in particular to a method for classifying histopathological images of colon cancer based on a multi-channel collaborative capsule network. Background technique [0002] With the rapid development of medical technology, medical images have been greatly expanded. The scientific use of medical image analysis to classify tissue and cell images efficiently and accurately can help doctors better explore cancer treatment methods. Medical image analysis is one of the most basic applications and one of the most active research fields in recent decades. By classifying tumor tissue at the cellular level, a better understanding of the characteristics of the tumor can be obtained, thereby helping their patients to better choose the means of treating their cancer. Classifying tissues and cells from colon cancer images is a challenging task as cells are not l...
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IPC IPC(8): G06V10/40G06V10/774G06V10/764G06K9/62
CPCG06V10/40G06V2201/03G06F18/241G06F18/214
Inventor 王万良李存发屠杭垚
Owner ZHEJIANG UNIV OF TECH