Focus detection model training method based on generative adversarial network
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A technology for detecting models and lesions, applied in the computer field, can solve problems such as inability to improve lesion detection performance, unsatisfactory expansion effect, etc.
Pending Publication Date: 2020-07-07
图玛深维医疗科技(北京)有限公司
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[0007] The purpose of this application is to provide a training method, device, equipment, and readable storage medium for a lesion detection model based on a generative confrontation network, so as to solve the problem that the traditional sample expansion scheme based on a generative confronta
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Embodiment 1
[0047] The following is an introduction to Embodiment 1 of a training method for a lesion detection model based on a generative adversarial network provided by the present application, see figure 1 , embodiment one includes:
[0048] S101. Obtain an original training sample, where the original training sample includes medical images and labeling information of the target tissue;
[0049] The purpose of this embodiment is to solve the problem of expanding the training samples of the lesion detection model under a small sample size and the unbalanced training samples. This embodiment can be applied to the detection of tumors in various tissues, for example, the detection of tumors in mammography, and the specific detection target can be determined according to the actual application scene. Specifically, a mammography image may be selected as the above medical image, and the detection of the target tissue may be realized based on this.
[0050] Specifically, after obtaining the...
Embodiment 2
[0060] Specifically, Embodiment 2 is mainly used to detect breast masses based on mammography images. see figure 2 , embodiment two specifically includes:
[0061] S201. Obtain an original training sample from a public dataset, where the original training sample includes a mammography target tissue image and labeling information;
[0062] Preferably, S201 includes the following process:
[0063] First, download or cooperate with the hospital to collect a certain amount of desensitized mammography data sets. In the implementation of this embodiment, the INbreast public dataset is used, mainly because this dataset has been extensively researched and used, and it is recognized for its accurate labeling effect, and it is easy to compare the pros and cons of the method with other methods horizontally.
[0064] Then, data cleaning is carried out. Through cleaning, the basic situation of the data set can be understood, and the basic information of the data can be grasped. Prefer...
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Abstract
The invention discloses a focus detection model training method based on a generative adversarial network, a device, equipment and a readable storage medium. According to the method, two ideas are adopted to reduce the requirements of the generative adversarial network for data: on one hand, a normal image, a focus and a focus mask are synthesized into three-channel image data to serve as the input of the network, and the three channels respectively contain different prior information, so that the difficulty of image generation is reduced; and on the other hand, a local evaluation mode is adopted in the evaluator of the generative adversarial network, and the requirement on the depth of the network is reduced in the mode, so that the requirement on the sample size is also reduced. Finally,the method achieves the purpose of completing the training of the generative adversarial network by using the small sample, thereby effectively expanding the training sample of the focus detection model, and facilitating the improvement of the detection performance of the focus detection model.
Description
technical field [0001] The present application relates to the field of computer technology, and in particular to a training method, device, equipment and readable storage medium of a lesion detection model based on a generative adversarial network. Background technique [0002] Worldwide, cancer remains the greatest threat to human life. Taking breast cancer as an example, breast cancer has always been the cancer with the highest incidence rate in women. Early treatment of breast cancer can not only prolong the life of patients, but also greatly reduce the overall expenditure of patients in terms of treatment costs. Therefore, artificial intelligence-based mammography-assisted diagnosis of breast cancer has gained more and more attention from doctors. [0003] In recent years, with the success of deep learning in natural images, people are also trying deep learning-based methods for cancer diagnosis and screening. However, deep learning methods based on convolutional neura...
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