Image segmentation method based on reverse attention network

Abstract

The invention discloses an image segmentation method based on a reverse attention network, and the method comprises the steps: firstly obtaining an image data set, and constructing a training set and a test set; then, creating a reverse attention network model, wherein the processing process of the reverse attention network model comprises the steps that the image is coded layer by layer through a plurality of convolutional layers to sequentially obtain output feature layers of different levels, and the output feature layers of the different levels are spliced and aggregated in parallel and then input into a decoder to be decoded to obtain a global feature image; inputting the global feature image and the output feature into a reverse attention network for processing until a low-level reverse attention feature is obtained; and inputting the training set into a reverse attention network model for training to obtain a trained reverse attention network model, and obtaining an image segmentation result. Advanced features obtained by image coding are input into the reverse attention network, so that the precision of image segmentation is greatly improved.

Description

technical field [0001] The invention belongs to the field of medical image processing, and in particular relates to an image segmentation method based on a reverse attention network. Background technique [0002] Colorectal cancer (CRC) is the third most common cancer in the world. Therefore, the prevention of colorectal tumors through colorectal tumor pre-examination has become a very important health examination worldwide. Colonoscopy can provide information on the location and appearance of colorectal polyps, allowing doctors to remove colon polyps before they develop into colorectal cancer, and is an effective colorectal cancer screening and prevention technique. Many studies have shown that early colonoscopy can help reduce the incidence of CRC by 30%. Therefore, clinically, accurate polyp segmentation is very important. However, since polyps often vary in appearance, such as size, color, and texture, even if they are the same type. [0003] In existing colonoscopic...

AI Technical Summary

Problems solved by technology

These issues lead to inaccurate segmentation of polyps and sometimes lead to missed detection of polyps

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