Training method and device for brain tumor recognition model based on semi-supervised learning

Abstract

The invention provides a training method and device for a brain tumor recognition model based on semi-supervised learning. The brain tumor recognition model comprises a detection network and a classification network, and the method comprises the following steps of obtaining a first training sample set and a second training sample set; performing unsupervised learning on the detection network and the classification network by using the first training sample set to generate a pre-trained detection network and a pre-trained classification network; training the pre-trained detection network and the pre-trained classification network by using the second training sample set to generate a trained detection network and a trained classification network; and outputting a trained brain tumor recognition model, wherein the trained brain tumor recognition model comprises the trained detection network and the trained classification network. According to the method, fine marking and coarse marking data are fully utilized in a semi-supervised learning mode, so a more robust deep convolutional neural network is obtained.

Description

technical field [0001] The present invention relates to the field of computer vision technology, in particular to medical image classification and deep learning technology, and more specifically, to a training method and device for a brain tumor recognition model based on semi-supervised learning. Background technique [0002] Medical images (such as CT images, MRI images, etc.) are an important type of data in the medical field, and play a pivotal role in assisting doctors in diagnosis and pathological research. The use of artificial intelligence technology to conduct intelligent and automatic analysis of medical images is of great significance to improving medical efficiency, saving medical costs, and reducing patient pain. Among them, the classification of medical images is one of the most basic tasks in the intelligent analysis of medical images. It has important requirements in various specific scenarios such as the identification of disease types, the judgment of the s...

AI Technical Summary

Problems solved by technology

However, due to the difficulty in obtaining and labeling medical image data, and the location and size of tumors in CT images are not fixed, tumor identification is also difficult, and the accuracy of brain tumor identification using models is low.

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