Image recognition model training method, device and system, and image recognition method, device and system

An image recognition and recognition model technology, applied in the computer field, can solve the problems of high cost and long time, and achieve the effect of improving efficiency, reducing cost and improving accuracy

Active Publication Date: 2020-03-24
TENCENT TECH (SHENZHEN) CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] At present, the training methods used in the endoscopic imaging diagnosis system use pre-labeled data in the iterative process, requiring a large amount of labele

Method used

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  • Image recognition model training method, device and system, and image recognition method, device and system
  • Image recognition model training method, device and system, and image recognition method, device and system
  • Image recognition model training method, device and system, and image recognition method, device and system

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Embodiment approach

[0096] The first implementation manner: according to each marked video image and the initial training image sample set, the image recognition model is obtained through retraining.

[0097] That is to say, in the embodiment of the present application, all previous initial training image sample sets and labeled image data can be combined to retrain the image recognition model. Since this method needs to restart the training of the image recognition model, the time cost may be relatively low. High, but it can ensure that the model training process fits the full amount of data.

[0098] The second implementation manner: update and train the initial image recognition model according to each labeled video image and the initial training image sample set.

[0099] That is to say, in the embodiment of the present application, iterative update can also be performed on the basis of the initial image recognition model, and the initial image recognition model can be further fine-tuned. For...

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Abstract

The invention relates to the technical field of computers. The invention particularly relates to an image recognition model training method, device and system, and an image recognition method, deviceand system. Each image is recognized according to the initial image recognition model, predicted lesion categories of the image images are respectively obtained, and the predicted lesion category is judged according to an image report associated with each image; the lesion category of each image is marked according to the judgment result; according to the marked image images and an initial training image sample set, iterative training is performed to obtain an image recognition model; and then the lesion category of the to-be-recognized image can be recognized based on the trained image recognition model, and the lesion category recognition result of the to-be-recognized image can be determined, so that iterative training is carried out by utilizing the image report, additional labeling cost does not need to be increased, the iterative rate is improved, and the recognition accuracy can be improved along with continuous iterative updating.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an image recognition model training and image recognition method, device and system. Background technique [0002] At present, the training methods used in the endoscopic imaging diagnosis system use pre-labeled data in the iterative process, requiring a large amount of labeled data, and these labeled data need to be labeled by doctors or experts, and the cost is relatively high. High and time-consuming, there is no relevant solution for this. Contents of the invention [0003] Embodiments of the present application provide an image recognition model training and image recognition method, device, and system, so as to improve the iteration rate of lesion recognition model training and reduce costs. [0004] The specific technical scheme that the embodiment of the present application provides is as follows: [0005] An embodiment of the present application provides ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V2201/03G06F18/24G06F18/214
Inventor 郑瀚尚鸿孙钟前
Owner TENCENT TECH (SHENZHEN) CO LTD
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