Training method of generative model, polyp identification method and device, medium and equipment

Abstract

The invention relates to a training method of a generative model, a polyp identification method and device, a medium and equipment, and the method comprises the steps: obtaining a training sample set, each training sample in the training sample set comprises a training image and a polyp marking type corresponding to the training image; obtaining a generated image and a restored image corresponding to the training image according to the training image and an image generation model; determining a first distribution distance corresponding to the training image and the generated image according to the training image and the generated image; according to the first distribution distance, the training image, the generated image, the restored image and a polyp marking category corresponding to the training image, determining target loss of the image generation model, wherein the target loss comprises first distribution loss determined according to the first distribution distance, and the first distribution loss and the first distribution distance are in a negative correlation relationship; and updating parameters of the image generation model according to the target loss under the condition that an updating condition is met.

Description

technical field [0001] The present disclosure relates to the field of image processing, and in particular, relates to a training method for generating a model, a method for identifying polyps, a device, a medium, and equipment. Background technique [0002] Endoscopes are widely used for colon screening and polyp detection, but the detection accuracy of endoscopes largely depends on the experience of endoscopists. However, because the characteristics of polyps are difficult to identify, and many polyps are small in size, the missed detection rate of polyp detection is relatively high, which greatly increases the difficulty of early polyp screening. [0003] In related technologies, the deep learning method can be used for model training to be used in a computer-aided diagnosis system for polyp identification and segmentation. However, when the out-of-sample data has a large domain shift through the above method, there may be a large performance gap in these trained networks...

AI Technical Summary

Problems solved by technology

However, when the out-of-sample data has a large domain shift through the above method, there may be a large performance gap in these trained networks, and it is difficult to ensure the generalization of the model through limited sample data, making the trained model Insufficient detection accuracy on out-of-sample data for accurate polyp detection

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