Tissue image recognition method and device, readable medium and electronic equipment

Abstract

The invention relates to a tissue image recognition method and device, a readable medium and electronic equipment, and relates to the technical field of image processing. The method comprises the steps: obtaining a tissue image collected by an endoscope, carrying out the preprocessing of the tissue image to obtain a target image, carrying out the recognition of the target image through a pre-trained recognition model, so as to determine the target type to which the tissue image belongs, wherein the recognition model is obtained through joint training with a preset comparison identification model according to a preset sample image set, the sample image set comprises a first number of labeled sample images with known types and a second number of unlabeled sample images with unknown types, and the first number is smaller than the second number; and comparing the structure of the recognition model to be the same as the structure of the recognition model, comparing neuron parameters of the recognition model, determining according to the neuron parameters of the recognition model, and if the target type indicates that the tissue image is an effective type, performing specified processing on the tissue image. The practicability and accuracy of the recognition model can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular, to a tissue image recognition method, device, readable medium and electronic equipment. Background technique [0002] The endoscope is equipped with optical lens, image sensor, light source and other components, which can enter the inside of the human body for inspection, so that doctors can intuitively observe the internal conditions of the human body, and have been widely used in the medical field. In the process of using an endoscope for inspection, many invalid images may be collected due to unstable lens entry techniques or improper position of the endoscope, such as obstructions, overexposure, and sharpness. Too low etc. These invalid images will interfere with the inspection results of the endoscope. Therefore, it is necessary to identify the images collected by the endoscope to ensure the effectiveness and accuracy of the endoscope inspection. Usuall...

AI Technical Summary

Problems solved by technology

However, labeling a large number of images requires a lot of manpower and material resources, and the work is complicated. Therefore, the number of labeled images is often very small, which reduces the accuracy of image recognition.

Images