Deep learning intestinal tract polyp segmentation method based on multi-scale information and parallel attention mechanism

A deep learning, multi-scale technology, applied in the field of deep learning image segmentation, can solve the problem of insufficient segmentation accuracy of intestinal polyps, and achieve the effect of shortening training time and accurate and effective classification

Pending Publication Date: 2021-03-12
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the problem of insufficient segmentation accuracy of intestinal polyps, the present invention proposes a deep learning intestinal polyp segmentation method based on multi-scale information and parallel attention mechanism

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  • Deep learning intestinal tract polyp segmentation method based on multi-scale information and parallel attention mechanism
  • Deep learning intestinal tract polyp segmentation method based on multi-scale information and parallel attention mechanism

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Embodiment Construction

[0035] In order to clarify the purpose, technical solutions and advantages of the present invention, the present invention will be further described in detail below in conjunction with specific embodiments and accompanying drawings.

[0036] refer to Figure 1 to Figure 8 , a deep learning intestinal polyp segmentation method based on multi-scale information and parallel attention mechanism, including the following steps:

[0037] Step 1: Obtain the picture to be segmented: the experimental data set of the present invention is from the public polyp data set CVC-ClinicDB, including polyp pictures of various types, shapes and colors;

[0038] Step 2: Use Res2Net deep convolutional neural network module and double compression excitation module (DoubleSqueeze and Excited, DSE) as the encoder to extract the features of the image;

[0039] Residual modules are fundamental modules in many modern backbone CNN architectures, such as figure 1 (a) shown. The Res2Net used in the presen...

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Abstract

The invention discloses a deep learning intestinal polyp segmentation method based on multi-scale information and a parallel attention mechanism, and the method comprises the steps: extracting features from finer granularity in a mode of building a branch during coding, and recalibrating a feature response through an improved compression excitation module; on the basis of pooling of the cavity space pyramid, further extracting and fusing features by establishing the relation between branches, multi-scale features of the intestinal tract and the polyp can be more accurately extracted and distinguished, so the problem that wrinkles of the intestinal tract wall are often misjudged as a polyp area during segmentation is well solved; during decoding, abandoning shallow features, refining the deep features, and further establishing a boundary relationship by using an attention mechanism, so a polyp boundary can be segmented more accurately on the basis of shortening training time.

Description

technical field [0001] The invention relates to the field of deep learning image segmentation, in particular to a deep learning intestinal polyp segmentation method based on multi-scale information and parallel attention mechanism. Background technique [0002] Gastrointestinal diseases are the most common human diseases, seriously affecting human life and health. According to statistics, among gastrointestinal diseases, colorectal cancer is the third most common cancer in the world after lung cancer and breast cancer, and intestinal polyps, as a high-risk precancerous disease, are the predecessor of colorectal cancer. Therefore, the prevention of colorectal cancer by detection and removal of preneoplastic polyps is crucial and a worldwide public health priority. Currently, colonoscopy is the "gold standard" technique for diagnosing colorectal adenomas and cancers. In my country, the annual demand for gastrointestinal endoscopy is more than 100 million, and it is widely us...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/12G06K9/62G06N3/04G06N3/08G16H30/20
CPCG06T7/12G06N3/084G16H30/20G06T2207/20081G06T2207/30032G06N3/045G06F18/253G06F18/214
Inventor 李胜王栋超何熊熊郝明杰夏瑞瑞程珊
Owner ZHEJIANG UNIV OF TECH
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