Medical image synthesis method, classification method and device based on adversarial neural network

Abstract

The invention belongs to the technical field of medical image processing, particularly relates to a medical image synthesis method, classification method and device based on an adversarial neural network, and aims to solve the problem that the accuracy cannot meet the requirements due to insufficient training samples in a brain disease classification task. The medical image synthesis method includes the steps: constructing a cyclic generative adversarial model comprising a category loss calculation function; training the cyclic generative adversarial model based on a first feature image set and a second feature image set; and when the sample classification loss satisfies a condition, taking an image generated by the cyclic generative adversarial model as sample data. According to the medical image synthesis method, the category loss is added on the basis of the Cycle-GAN network model, so that the synthesized brain image is more real, and the sample size is increased by two times in anunsupervised mode, and the problem of insufficient sample size in the brain disease classification process by using a deep learning method is solved, and the classification accuracy can be improved.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a medical image synthesis method, classification method and device based on an adversarial neural network. Background technique [0002] With the development of science and technology, a variety of machine learning technologies including deep neural networks have been successfully applied in many fields, such as computer vision, speech recognition, natural language processing, automatic driving, cancer detection, etc. More and more studies expect to use deep learning methods to further improve the diagnostic accuracy of brain diseases and realize computer-aided diagnosis. In-depth learning methods can achieve breakthrough development in many fields. In addition to the improvement and innovation of algorithms and powerful computing resources, an important reason is the availability of massive training samples. However, in the field of medical imaging,...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the above technical problems, that is, in order to solve the problem that the accuracy cannot meet the demand caused by insufficient training samples in the brain disease classification task, the present invention proposes a medical image synthesis method, classification method and device based on an adversarial neural network

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