Cross-modal medical image domain adaptive classification method based on graph neural network

Abstract

The invention discloses a cross-modal medical image domain adaptive classification method based on a graph neural network. The collection and marking of cross-modal medical image data are affected because a cross-modal medical image presents a larger intra-class difference and a smaller inter-class difference. The graph neural network module can well distinguish medical images in different fields,model oscillation can be greatly reduced, and classification precision is improved.

Description

technical field [0001] The invention relates to the technical field of medical image classification, in particular to a cross-modal medical image domain adaptive classification method based on a graph neural network. Background technique [0002] Cancer is still one of the leading causes of human death, and timely and accurate disease grading plays a vital role in later treatment. Although medical imaging technology has developed rapidly in recent years, computer-aided diagnosis systems (CAD) usually rely on training machine learning models (such as deep convolutional neural networks) and a large number of correctly labeled samples, and the lack of large training data sets presents significant challenges to computer-aided diagnosis (CAD) systems. First, to gather data, experienced medical imaging experts need to scrutinize hundreds to thousands of gigabytes of images, an annotation process that is time-consuming and labor-intensive. Secondly, it is difficult to label medic...

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Problems solved by technology

[0004] The above introduction to existing methods shows that the current domain adaptation methods designed for medical image analysis are mai

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