Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Intracranial hemorrhage subtype classification algorithm based on bilinear pooling applied to CT images

A CT image and intracranial hemorrhage technology, applied in the field of intelligent medical image processing, can solve the problems of high dependence and time-consuming doctor's professionalism, achieve the effect of excellent classification performance, improve the ability of feature expression, and solve the problem of sample mining

Active Publication Date: 2022-01-14
JILIN UNIV
View PDF19 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the subtype classification of intracranial hemorrhage is time-consuming and highly dependent on the professionalism of doctors, the present invention provides a subtype classification algorithm of intracranial hemorrhage based on bilinear pooling applied to CT images
The present invention proposes a new CNN-RNN network architecture for subtype classification of ICH. In the CNN module, starting from the characteristics of intracranial CT images, this problem is viewed from a fine-grained perspective, which solves the problems of ResNet and DenseNet networks. Defects on intracranial CT images

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
  • Intracranial hemorrhage subtype classification algorithm based on bilinear pooling applied to CT images
  • Intracranial hemorrhage subtype classification algorithm based on bilinear pooling applied to CT images
  • Intracranial hemorrhage subtype classification algorithm based on bilinear pooling applied to CT images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings, but it is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be covered by the present invention. within the scope of protection.

[0041]The present invention provides an intracranial hemorrhage subtype classification algorithm based on bilinear pooling applied to CT images. This algorithm innovatively solves the problem of intracranial hemorrhage subtype classification from the perspective of fine-grained classification. The CNN part uses The compact bilinear pooling network architecture improves the feature expression ability of the neural network and improves the classification performance. Bilinear pooling has been proven to be an effective method for solving fine-grained 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 discloses a subtype classification algorithm of intracranial hemorrhage applied to CT images based on bilinear pooling. The algorithm innovatively solves the problem of subtype classification of intracranial hemorrhage from the perspective of fine-grained classification, and is used in the CNN part. The network architecture of compact bilinear pooling has been improved, the feature expression ability of neural network has been improved, and the classification performance has been improved. Bilinear pooling has been proven to be an effective method for solving fine-grained classification problems. It treats features from two CNNs from different sources or CNNs from the same source as two different features by modeling high-order statistics. And calculate the outer product of the two features, and then perform feature fusion through the pooling operation to capture the relationship between different features, and then obtain a stronger global feature representation. The method uses image translation invariance to interactively model local paired features.

Description

technical field [0001] The invention belongs to the field of intelligent medical image processing, and relates to a bilinear pooling-based classification algorithm applied to subtypes of intracranial hemorrhage. Background technique [0002] Intracranial hemorrhage (ICH) is a bleeding disorder that occurs in the ventricle or brain tissue. Intracranial hemorrhage includes: intraventricular hemorrhage (IVH), parenchymal hemorrhage (IPH), subarachnoid hemorrhage (SAH), epidural hemorrhage (EDH) and subdural hematoma (SDH). The study of subtype classification of intracranial hemorrhage is to judge whether there is intracranial hemorrhage in CT images and to classify its five subtypes (IPH, IVH, EDH, SDH, SAH). Diagnosing intracranial hemorrhage is a major challenge in medicine. Determining the site and type of intracranial hemorrhage is key to treating the patient. The current method of diagnosing intracranial hemorrhage is a CT scan of the brain. Because tissues such as bra...

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 Patents(China)
IPC IPC(8): G16H30/20G06K9/62G06N3/04G06V10/764G06V10/774G06V10/80G06V10/82
CPCG16H30/20G06V2201/03G06N3/044G06N3/045G06F18/25G06F18/24G06F18/214
Inventor 刘萍萍石立达宁港军周求湛
Owner JILIN 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
Eureka Blog
Learn More
PatSnap group products