Medical image processing method based on attention mechanism and related equipment

Abstract

The invention provides a medical image processing method based on an attention mechanism and related equipment, and the method comprises the steps: obtaining a three-dimensional medical image of a to-be-classified object, inputting the three-dimensional medical image into a feature extraction model for feature extraction, and outputting a three-dimensional feature map of the three-dimensional medical image; wherein the feature extraction model is obtained by training a three-dimensional residual convolutional network based on a double attention mechanism, and the double attention mechanism comprises a channel attention mechanism and a depth attention mechanism, so that the feature extraction model can extract deep semantic feature information of the three-dimensional medical image; the accuracy and the comprehensiveness of the three-dimensional feature map are improved, so that the three-dimensional feature map is subjected to pooling processing, the obtained target feature vector is input into the classification model, and the category information of the three-dimensional medical image can be quickly and accurately obtained.

Description

technical field [0001] The present application mainly relates to the technical field of image processing, and more specifically relates to an attention mechanism-based medical image processing method and related equipment. Background technique [0002] With the development of various image processing technologies, there are more and more researches on the processing and analysis of medical images. For example, in the novel coronavirus pneumonia (Corona Virus Disease 2019, COVID-19, referred to as "new coronary pneumonia") epidemic, in order to detect patients infected with new coronary pneumonia in a timely and accurate manner, computerized tomography (CT) images of the lungs are usually detected, using The deep learning / machine learning algorithm in artificial intelligence technology (AI) analyzes CT images and determines the category of each CT image to assist medical staff in determining the cause and giving an appropriate treatment plan. [0003] However, the accuracy o...

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Problems solved by technology

[0003] However, the accuracy of current image recognition models for CT image classification and recognition is often relatively low, which affects the reliability of auxiliary diagnosis based on target area recognition results.

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