Symmetric residual U-shaped network breast mass segmentation method based on composite weighted loss function

A loss function and composite technology, applied in the field of deep learning image processing, can solve problems such as increasing the complexity of the process and not conforming to the stable and efficient development direction of the computer-aided diagnosis system

Pending Publication Date: 2022-03-18
SOUTHWEAT UNIV OF SCI & TECH
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the two-stage network relies too much on the accuracy of the detection process in terms of segmentation accuracy, and increase...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Symmetric residual U-shaped network breast mass segmentation method based on composite weighted loss function
  • Symmetric residual U-shaped network breast mass segmentation method based on composite weighted loss function
  • Symmetric residual U-shaped network breast mass segmentation method based on composite weighted loss function

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] At present, the research on mass segmentation of mammography images based on deep learning methods focuses more on the region of interest (ROI) of mammography segmentation. The two-stage process of "re-segmentation" also uses the deep learning detection model, and then uses the detected ROI as the input of the segmentation network, which can greatly reduce the difficulty of the segmentation process. However, the two-stage network relies too much on the accuracy of the detection process in terms of segmentation accuracy, and increases the complexity of the process, which is not in line with the stable and efficient development direction of computer-aided diagnosis systems. Therefore, this patent will directly segment the mass based on the full mammogram.

[0035] The embodiment of the present application provides a research on mammography image mass segmentation based on a deep learning method, which is used to improve the accuracy, high efficiency and reduce missed diag...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a breast lump segmentation method based on deep learning. According to the scheme, a deep neural network is trained by training a manually labeled lump image. After a complete mammary gland image is input, the network can autonomously learn imaging features of the lumps, and an output result is an area which is identified as the lumps by the network, so that end-to-end mammary gland lump segmentation is realized. In order to improve the detection rate of the lumps, the invention discloses a novel weighted compound loss function.

Description

technical field [0001] The invention relates to a deep learning image processing technology, and specifically designs a method for segmenting masses of mammography images. Background technique [0002] The mammary gland is composed of skin, fibrous tissue, mammary glands and fat. Breast cancer is a malignant tumor that occurs in glandular epithelial tissue. Among them, women accounted for 99%, and men accounted for only 1%. Early breast cancer usually has no obvious clinical symptoms, and mostly manifests as a painless mass or mild breast pain. The "China Breast Disease Survey Report" shows that only 5% of women undergo breast disease examination once a year, and 23% of women think that There is no need for special breast disease examination, coupled with the insufficient promotion of breast disease census in my country, most grassroots medical institutions lack professional medical personnel and census equipment, resulting in a low early detection rate of breast cancer in m...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/00G06V10/26G06V10/82G06N3/04
CPCG06T7/0012G06T2207/30068G06N3/045
Inventor 周雨薇刘志勤王庆凤黄俊周莹徐卫云
Owner SOUTHWEAT UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap