Medical image data generation method and device for artificial neural network training

Abstract

The invention provides a medical image data generation method and device for artificial neural network training, and the method comprises the steps: building the corresponding relation between the target features of a basic medical image and the image features of an extended image through the self-learning capability of an artificial neural network; wherein the image features comprise texture features and content features; obtaining a current target feature of the current basic medical image; determining an image feature of a current extended image corresponding to the current target feature through the corresponding relationship; specifically, the method determines the image features of the current extended image corresponding to the target features, and comprises the steps: determining the image features of the extended image corresponding to the target features, which are the same as the current target features, in the corresponding relation as the image features of the current extended image, thereby improving the rationality of the generated extended image; details are restored more excellently, and the diversity of features is improved.

Description

technical field [0001] The present application relates to the field of medical detection, in particular to a method and device for generating medical image data for artificial neural network training. Background technique [0002] In recent years, with the increase of computing power and the explosive increase of data, artificial intelligence technology has made great progress, and the representative technology is deep learning. And began to be applied to various fields in life and production. In the field of medical imaging, due to the instability of human expert experience, deep learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and treatment and reduce the imbalance of medical resources. The great progress of deep learning in the field of computer vision has inspired its application in medical image analysis, such as image classification, image segmentation, image registration, lesion detection and other auxi...

AI Technical Summary

Problems solved by technology

However, traditional enhancement techniques often destroy the integrity of the image, which is unfavorable for medical images, because medical images themselves have fewer features than natural images, and adding noise or clipping will be even more unfavorable for network training.

Simply using the generative confrontation network for generation also requires a large data set as a support, which is better for natural images, but small sample medical images often fail to achieve the desired effect

Images