Medical image noise data identification method based on artificial intelligence and related device

A noise data and medical imaging technology, applied in the field of data processing, can solve problems such as low accuracy, difficulty in meeting noise data identification requirements, and inability to determine it as an outlier

Active Publication Date: 2020-07-14
TENCENT TECH (SHENZHEN) CO LTD
View PDF5 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, many noisy data do not belong to outliers under the statistical rules. For example, a medical image with a lesion identified by a label does not show a lesion, but shows a wound formed after surgery on the lesion area. Label data should belong to noise data, but i

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
  • Medical image noise data identification method based on artificial intelligence and related device
  • Medical image noise data identification method based on artificial intelligence and related device
  • Medical image noise data identification method based on artificial intelligence and related device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] Embodiments of the present application are described below in conjunction with the accompanying drawings.

[0027] In order to more effectively screen out medical images with wrong labels, an embodiment of the present application provides a noise data recognition method for medical images based on artificial intelligence. Perform identification, and determine whether the medical image is noise data through the confidence reflected in the identification result corresponding to the medical image, so that medical images with wrong labels can be effectively screened out.

[0028] The noise data recognition method of medical images provided in the embodiment of the present application is realized based on artificial intelligence. Artificial Intelligence (AI) is to simulate, extend and expand human intelligence by using a digital computer or a machine controlled by a digital computer to perceive the environment, Theories, methods, techniques and application systems for acquir...

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 embodiment of the invention discloses a medical image noise data identification method based on artificial intelligence and a related device. For a medical image with a label, if the medical imagewith the label needs to identify whether noise data exists in the medical image, the medical image can be taken as to-be-identified label data and be identified through a first identification model.The first identification model has a good anti-noise capability. According to the identification result obtained by the first identification model, the reflected confidence coefficient can effectivelyexpress the possibility that the to-be-identified label data belongs to the noise data, so that the medical images with wrong label annotations are effectively screened out from the medical images with labels, and the determination accuracy of the noise data is improved.

Description

technical field [0001] This application relates to the field of data processing, in particular to a noise data identification method and related devices based on artificial intelligence for medical images. Background technique [0002] During network model training, labeled data can be used as training samples for supervised or semi-supervised training of the network model. Label data can be understood as data carrying a label, which is used to identify the specified content embodied and involved in the data. For example, in the label data about the identification of the lesion area in the medical image, the label of the medical image can reflect the data in the medical image. Whether there is a lesion area, or the lesion type of the lesion area, etc. [0003] However, not all data labels are accurate. If the label of a label data is actually incorrect, that is, the content identified by the label is not reflected in the data, or the data does not involve the content identi...

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): G06K9/62G06N3/04G16H30/40
CPCG16H30/40G06N3/045G06F18/214
Inventor 陈豪尚鸿孙钟前
Owner TENCENT TECH (SHENZHEN) CO LTD
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