Training method of abnormal region image generation network and related products

Abstract

The invention relates to a training method of an abnormal region image generation network and a related product. The method comprises the steps of acquiring a training sample image, wherein the training sample image has an abnormal area; inputting the training sample image into an initial abnormal region image generation network to obtain an initial abnormal region image, and fusing the initial abnormal region image with the training sample image to obtain a fused image; wherein the fused image comprises a preset simulation mark; inputting the fused image and the real sample image into an initial discrimination network to obtain a discrimination result of the fused image; calculating the loss between the discrimination result and the simulation mark by adopting a loss function, and training the initial discrimination network and the initial abnormal region image generation network according to the loss; and when the value of the loss function reaches convergence, completing training ofthe initial abnormal region image generation network to obtain an abnormal region image generation network. The method can improve the precision and training efficiency of the abnormal region image generation network obtained by training.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to a training method and related products of an abnormal area image generation network. Background technique [0002] In the medical field, brain diseases refer to the general term of inflammation, vascular diseases, tumors, lesions, deformities, genetic diseases, etc. of intracranial tissues and organs (such as meningeal vessels, brain stem, cranial nerves, etc.). Brain diseases will be reflected in brain medical images, such as brain tumors, cerebral hemorrhage, Parkinson's disease, Alzheimer's disease, etc. Magnetic resonance imaging (NuclearMagnetic Resonance Imaging, MRI) is widely used in the diagnosis of brain diseases because of its non-radioactive and high-quality imaging of brain structures. Computer Aided Diagnosis (CAD) can effectively Screening patients with brain diseases greatly reduces the workload of doctors and improves the accuracy of detection...

AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to provide a training method and related products for the abnormal area image generation network to solve the problem of low accuracy of the segmentation results of the deep learning segmentation network learned in the traditional technology.

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