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

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 ...

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 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

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