Mammary gland lump segmentation method based on cross attention mechanism

Abstract

The invention relates to an X-ray auxiliary diagnosis technology, and aims to provide a mammary gland lump segmentation method based on a cross attention mechanism. The method comprises the steps: making a data set; preprocessing a cross attention mechanism; constructing a deep convolutional neural network; adjusting the pre-training weight distribution of the image network data set; adjusting pre-training weight distribution by utilizing a data preprocessing result, and training a deep convolutional neural network; and reasoning the X-ray image to be detected by using the deep convolutional neural network. According to the method, the model is quickly trained by using the cross attention mechanism, and the network can select the lump features from the MLO position and the CC position through the cross attention mechanism so as to learn and adjust the cross attention weight value. Compared with the conventional method for judging the lump only from the CC position or the MLO position,the lump on the mammary gland X-ray image can be rapidly, efficiently and automatically segmented, the detection rate and accuracy of the lump in the mammary gland X-ray image are improved, and the method has higher practical application and popularization value.

Description

technical field [0001] The invention relates to mammary gland X-ray auxiliary diagnosis technology, in particular to a mammary gland mass segmentation method based on a cross attention mechanism (CrossAttention). Background technique [0002] Breast cancer has grown to be one of the most common cancers in women. Accurate identification of breast masses is a necessary condition for the diagnosis of breast diseases. Due to the interference of high-density and iso-density glandular tissue in the breast, it is extremely difficult to identify masses in mammary X-rays with the naked eye. Physician a lot of energy and time. Using artificial intelligence to learn a large number of tumor X-ray samples can read the film faster, locate the tumor more accurately, and lay a solid foundation for doctors to judge whether the tumor is benign or malignant. [0003] In clinical work, the bilateral mammary glands are generally taken for radiographic examination, and usually two positions are...

AI Technical Summary

Problems solved by technology

In the diagnosis of breast X-ray, it is necessary to combine the MLO and CC positions to comprehensively judge whether there is a mass in the image. However, the existing technology can only judge the mass from one of the MLO or CC positions, which is very easy to cause missed detection and false detection. Clinically required accuracy

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