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

Abstract

The present disclosure relates to a training method for generating a model, a polyp identification method, device, medium and equipment, the method comprising: obtaining a training sample set, wherein each training sample in the training sample set includes a training image and a polyp corresponding to the training image Labeling categories; according to the training image and the image generation model, obtain the generated image and the restoration image corresponding to the training image; according to the training image and the generated image, determine the first distribution distance corresponding to the training image and the generated image; according to the first distribution distance, the training image , generating images, restoring images, and polyp labeling categories corresponding to training images, and determining the target loss of the image generation model, wherein the target loss includes a first distribution loss determined according to the first distribution distance, and the first distribution The loss is negatively correlated with the first distribution distance; if the update condition is met, the parameters of the image generation model are updated according to the target loss.

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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