Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Unknown disease category identification method and device based on centralized space learning

A centralized and category-based technology, applied in the computer field, can solve problems such as lack of unknown category detection capabilities

Pending Publication Date: 2020-10-27
HANGZHOU SHENRUI BOLIAN TECH CO LTD +1
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the ability to detect unknown classes is missing

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
  • Unknown disease category identification method and device based on centralized space learning
  • Unknown disease category identification method and device based on centralized space learning
  • Unknown disease category identification method and device based on centralized space learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0019] The core of the present invention is to propose a centralized latent space, which is composed of a central point located in the central area and surrounding prototype points. Samples of known classes are clustered around the prototype points of each class, while samples of unknown classes are clustered around the center point. Based on the sample's distance from the nearest prototype point, samples of known and unknown class...

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 an unknown disease category identification method and device based on centralized space learning. The method comprises the steps of training an initial model through known category samples, initializing a known category space, and mapping the known category samples to a hyperspherical surface of a hidden space; training a generative adversarial network; generating an unknownanchor point; generating an unknown image corresponding to the unknown anchor point based on the unknown anchor point and the adversarial network; taking the unknown image as an unknown category sample, combining the unknown category sample with a known category sample, and adjusting a known category space to obtain a trained model; and obtaining a new sample, extracting features of the new sample based on the trained model, calculating the distance from the features of the new sample to each known category prototype point, if the distance is less than a preset threshold, determining that thenew sample belongs to a category corresponding to the known category prototype point, and if the distance is greater than or equal to the preset threshold, determining that the new sample belongs toan unknown category.

Description

technical field [0001] The present invention relates to the field of computers, in particular to a method and device for identifying unknown disease categories based on centralized spatial learning. Background technique [0002] With the rapid development of convolutional neural networks, CAD (Computer Aided Diagnosis System) has developed rapidly, achieved excellent performance, and carried out a wide range of clinical applications. However, all existing CAD solutions assume that pre-defined disease categories include all target categories and make predictions based on this set of predefined disease categories. This method is accompanied by two risks: 1. For diseases that are not included in the training set (such as intractable diseases or new disease categories), directly identifying them as predefined categories will lead to misdiagnosis and even delay treatment, resulting in unavailable The consequences of acceptance; 2. Due to the huge workload of the imaging departme...

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): G16H50/20G16H50/70
CPCG16H50/20G16H50/70
Inventor 史业民于重之俞益州
Owner HANGZHOU SHENRUI BOLIAN TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com