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Endoscope image classification model training method, and image classification method and device

A classification model and training method technology, applied in the direction of image analysis, image enhancement, image data processing, etc., can solve the problem of huge cost, achieve the effect of reducing calculation amount, saving labeling cost, and enhancing classification accuracy

Active Publication Date: 2021-10-12
BEIJING BYTEDANCE NETWORK TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these works are based on fully supervised methods, which require a large amount of labeled data, and the cost of labeling data is huge

Method used

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  • Endoscope image classification model training method, and image classification method and device
  • Endoscope image classification model training method, and image classification method and device
  • Endoscope image classification model training method, and image classification method and device

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Embodiment Construction

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts also fall within the protection scope of the present application.

[0043] The terms used in this specification are those general terms currently widely used in the art in consideration of functions about the present disclosure, but the terms may be changed according to the intention of those of ordinary skill in the art, precedents, or new technologies in the art. Also, specific terms may be selected by the applicant, and in this case, their detailed meanings will be described in the detailed description of the present disclosure. ...

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Abstract

The invention discloses an endoscope image classification model training method, and an image classification method and device. The method comprises the following steps: acquiring a first image set, wherein the first image set is a set of first modal images of one or more objects acquired by an endoscope operated in a first modal; acquiring a second image set, wherein the second image set is a set of second modal images of the one or more objects acquired by an endoscope operated in a second modal different from the first modal, and the second modal images is in one-to-one correspondence with the first modal images; and inputting the first image set and the second image set as training data sets into the endoscope image classification model, and training the endoscope image classification model to obtain a trained endoscope image classification model.

Description

technical field [0001] The present application relates to the field of artificial intelligence, in particular to a training method of an endoscope image classification model based on comparative learning, an endoscope image classification method, a device, and a computer-readable medium. Background technique [0002] Most colorectal cancers start as growths on the surface of the lining of the colon, called polyps, and some polyps can develop into cancer. Therefore, early detection and identification of polyp types is crucial for cancer prevention and treatment. However, visual classification of polyps is challenging, as different endoscopic lighting conditions, varying texture, and appearance can lead to identification difficulties. [0003] In order to reduce the burden on doctors, some work attempts to automatically realize the identification of polyp types using deep learning. However, these works are based on fully supervised methods, which require a large amount of la...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/04G06N3/08G06T2207/10068G06T2207/20081G06T2207/20084G06F18/22G06F18/214G06F18/24
Inventor 边成李永会杨延展
Owner BEIJING BYTEDANCE NETWORK TECH CO LTD