Medical image synthesis and classification method based on a conditional multi-judgment generative adversarial network

Abstract

The invention discloses a medical image synthesis and classification method based on a conditional multi-judgment generative adversarial network. The method comprises the following steps: 1, segmenting a lesion area in a computed tomography (CT) image, and extracting a lesion interested area (Region of Interest, ROIs for short); 2, performing data preprocessing on the lesion ROIs extracted in thestep 1; 3, designing a Conditional Multi-Discriminant Generative Adversarial Network (Conditional Multi-) based on multiple conditions The method comprises the following steps: firstly, establishing aCMDGAN model architecture for short, and training the CMDGAN model architecture by using an image in the second step to obtain a generation model; 4, performing synthetic data enhancement on the extracted lesion ROIs by using the generation model obtained in the step 3; and 5, designing a multi-scale residual network (Multiscale ResNet Network for short), and training the multi-scale residual network. According to the method provided by the invention, the synthetic medical image data set with high quality can be generated, and the classification accuracy of the classification network on the test image is relatively high, so that auxiliary diagnosis can be better provided for medical workers.

Description

technical field [0001] The invention relates to medical image synthesis and classification of conditional multi-discriminant generation confrontation network Background technique [0002] In recent years, relying on the powerful layered feature extraction capability of Convolutional Neural Network (CNN), the image classification effect has surpassed that of humans in many aspects. One of the main reasons is the use of large-scale labeled datasets to Train deep neural networks for computer vision tasks such as handwriting recognition. However, there is still a lot of room for improvement in some aspects, such as the classification of specific areas of medical images. One of the biggest challenges is that it is impossible to obtain a large-scale medical image dataset, and the deep neural networks trained with small-scale datasets The network will not be able to learn some features in medical images, which will make its classification effect poor. [0003] One of the biggest ...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that the existing network model does not perform well in medical image classification, such as the inability to obtain a large number of medical images, images without real labels, etc.

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