Cervix uteri OCT image classification method and system based on two-way attention convolutional neural network

Abstract

The invention discloses a cervical OCT image classification method based on a double-channel attention convolutional neural network. On the basis of a convolutional neural network architecture, two attention mechanisms are added and realized, so that the incidence relation between features with relatively long distances on image pixels can be better captured, the weights of different high-dimensional features are learned, and accurate classification of the cervical 3D OCT images is realized. The method comprises the following steps: 1) introducing two attention mechanisms into a convolutionalneural network; 2) introducing a channel attention mechanism, preferentially using global average pooling to extract channel features of the 2D OCT image, and then using a multi-layer perceptron to learn weights of channels; 3) introducing a spatial attention mechanism, referring to a self-attention mechanism, and calculating the similarity between each feature in the feature map and other features to realize similarity calculation of non-adjacent image regions; 4) performing downsampling on the features by using global average pooling, then adding two full connection layers, and finally performing classification by using a softmax function.

Description

technical field [0001] The invention provides a cervical OCT image classification method based on a two-way attention convolutional neural network, which belongs to the fields of medical image analysis and computer-aided diagnosis. Background technique [0002] Cervical cancer is one of the most common malignancies in women worldwide. In 2018, there were about 569,000 new cases of cervical cancer worldwide, and about 311,000 deaths. In the past two decades, with the widespread application of cervical liquid-based thin-layer cytology test (thinprepcytologic test, TCT) and human papillomavirus (human papillomavirus, HPV) test, cervical cancer can be effectively prevented in the early stage, so in developed countries Significantly reduced morbidity and mortality. However, in poor and developing countries, cervical cancer remains associated with high morbidity and mortality due to limited access to cervical cancer screening services and lack of HPV vaccination. For example, i...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the problem of poor classification effect in the prior art, the present invention provides a cervical OCT image classification method based on a two-way attention convolutional neural network

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