Convolutional neural network automatic segmentation method and system for mammary molybdenum target data set

A convolutional neural network and convolutional neural technology, applied in the field of automatic segmentation and system of convolutional neural network for mammography data set, can solve the problems of squeezing the learning ability of deep learning model, troubled landing, model time-consuming, etc. Achieve the effect of improving test speed, ensuring accuracy and improving practicability

Inactive Publication Date: 2019-07-26
SHANDONG UNIV
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, a robust deep learning model requires a large amount of data from different data sources as a training set, and the amount of high-quality medical data that is often available is small. Therefore, how to train a deep learning model on small medical data is more robust. It is also one of the main factors that hinder the implementation of this method
In addition, in order to further squeeze the learning ability of the deep learning model, the design of the model structure tends to have deeper layers and more parameters. Practicality forms a contradiction

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
  • Convolutional neural network automatic segmentation method and system for mammary molybdenum target data set
  • Convolutional neural network automatic segmentation method and system for mammary molybdenum target data set
  • Convolutional neural network automatic segmentation method and system for mammary molybdenum target data set

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0055] This embodiment provides a convolutional neural network automatic segmentation method applied to a small breast mammography data set, which realizes the automatic segmentation of a deep convolutional neural network in a breast mammography data set, and utilizes a knowledge distillation method based on attention transfer, While retaining the characteristics of the domain, the large model trained on the big data is compressed into a small model, and then fine-tuned on the small data set, so as to ensure the accuracy of the model on the small data set and greatly reduce the parameters of the model.

[0056] Please refer to the attached figure 1 , a convolutional neural network automatic segmentation method applied to a small mammography data set proposed in this embodiment includes the following steps:

[0057] S101, selecting a large convolutional neural network to pre-train it in a large breast mammography data set.

[0058] Specifically, in the step 101, a fully convol...

Embodiment 2

[0108] This embodiment provides a convolutional neural network automatic segmentation system applied to a small breast mammography data set, the system comprising:

[0109] The model training module is configured to pre-train the large convolutional neural network on the mammography large data set;

[0110] The model compression module is configured to perform model compression on the trained large convolutional neural network by using attention transfer and knowledge distillation methods to obtain a small convolutional neural network;

[0111] A model fine-tuning module configured to fine-tune a small convolutional neural network on a small mammography dataset.

[0112] In this embodiment, the model training module is specifically configured as:

[0113] Construct a large-scale mammography data set;

[0114] Select a fully convolutional neural network;

[0115] On the large breast mammography data set, the convolutional neural network is pre-trained through the backpropaga...

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 convolutional neural network automatic segmentation method and system for a mammary molybdenum target data set, which can obviously reduce model parameters and improve the practicability while ensuring the precision of a deep learning model on a mammary molybdenum target small data set. The method comprises the following steps: pre-training a convolutional neural big network on a mammary molybdenum target big data set; performing model compression on the trained convolutional neural large network by adopting attention transfer and knowledge distillation methods to obtain a convolutional neural small network; and carrying out fine tuning on the convolutional neural small network on the mammary molybdenum target small data set.

Description

technical field [0001] The disclosure relates to an automatic segmentation method and system of a deep fully convolutional neural network based on an attention transfer-based knowledge distillation method applied to a small breast mammography data set. Background technique [0002] Breast cancer is the cancer with the highest incidence rate in women. Studies have shown that breast cancer accounts for 29% of cancer incidence and 15% of cancer mortality in women. Early diagnosis of breast cancer is crucial to the survival of patients. Among various breast cancer early screening techniques, mammography has the advantages of low dose, high sensitivity, simplicity and convenience. Radiologists often misdiagnose cancer or overdiagnose cancer due to differences between entities and observers during mammography analysis. Therefore, computer-aided diagnosis is very important as a pre-screening method for disease diagnosis. significance. At the same time, due to the large difference...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06T7/00
CPCG06T7/0012G06N3/084G06T2207/30068G06N3/045G06F18/214
Inventor 刘伯强孙辉陈威孙佳伟彭苏婷
Owner SHANDONG UNIV
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