Multi-task cervical cancer cell automatic identification method and device

A technology for automatic identification of cervical cancer cells, applied in the field of multi-task automatic identification methods and devices for cervical cancer cells, capable of solving problems such as false positives, false detections, and image-level false positives

Inactive Publication Date: 2020-10-02
湖南品信生物工程有限公司
View PDF1 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the detection of cervical cancer cells based on the deep learning target detection network model will cause a large number of false positives, that is, a large number of diseased cells are detected, but in fact these detected diseased cells are

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
  • Multi-task cervical cancer cell automatic identification method and device
  • Multi-task cervical cancer cell automatic identification method and device
  • Multi-task cervical cancer cell automatic identification method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] The following are specific embodiments of the application and in conjunction with the accompanying drawings, further describe the technical solution of the application, but the application is not limited to these embodiments. In the following description, specific details, such as specific configurations and components, are provided merely to help a comprehensive understanding of the embodiments of the present application. Accordingly, those of ordinary skill in the art should recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0044] It should be noted that, in the case of no conflict, the implementations in the present application and the features in the implementations can be combined with each other.

[0045] figure 1It is a flow chart of a multi-task cer...

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 provides a multi-task cervical cancer cell automatic identification method and device, and relates to the technical field of cell image identification, and the method comprises: traininga multi-task network model, wherein the multi-task network model comprises a classification branch and a detection branch; enabling the detection branch to be realized based on a deep learning targetdetection model YOLOv3 and be capable of be used for detecting cancer cells in the cervical cell image; enabling the classification branch to be used for judging whether abnormal cells exist in the cervical cell image or not; judging whether abnormal cells exist in the cervix uteri cell image or not by adopting classification branches, judging that the cervix uteri cell image is positive if the abnormal cells exist, and otherwise, judging that the cervix uteri cell image is negative; detecting the cell type in the cervical cell image by adopting a detection branch, and generating corresponding prediction information, wherein the detected cell types at least comprise cancer cells; and when the classification branch judges that the cervical cell image is positive, outputting a detection result of the detection branch. The multi-task cervical cancer cell automatic identification method improves the accuracy of cervical cancer cell automatic identification.

Description

technical field [0001] The present application relates to the technical field of cell image recognition, in particular to a multi-task automatic cervical cancer cell recognition method and device. Background technique [0002] Cervical cancer is the most common gynecological malignancy. Cervical cancer cell identification, as one of the key technologies in the prevention and treatment of cervical cancer, is of great significance to reduce the mortality of women from cervical cancer. In some existing technical solutions, a network model based on deep learning target detection is used to realize the identification of cervical cancer cells; for example, Chinese patent CN201810169962.8 discloses a method and device for processing cervical cell images, which includes: Step S1: Obtain a sample image, and then obtain the calibration information of the sample image corresponding to the sample image; Step S2: Generate a sample set according to the sample image and the calibration inf...

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): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/08G06T2207/20084G06T2207/20081G06T2207/30024G06N3/045G06F18/2415G06F18/214
Inventor 梁毅雄潘长立刘剑锋王慧
Owner 湖南品信生物工程有限公司
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