Digestive tract endoscope image processing method and device, storage medium, equipment and system

Abstract

The invention discloses a digestive tract endoscope image processing method and device, a storage medium, equipment and a system, belongs to the technical field of artificial intelligence, and relatesto a computer vision technology and a machine learning technology. The digestive tract endoscope image processing method comprises the following steps: acquiring a to-be-detected digestive tract endoscope image; classifying the digestive tract endoscopic images to be detected based on a first model, the first model being obtained by training based on a first training data set under the constraintof a second model, the first training data set comprising a pure data set and a noise data set, and the second model being obtained by training based on a second training data set before training thefirst model; wherein the pure data set comprises sample images with consistent annotations, and the noise data set comprises sample images with inconsistent annotations, and the second training dataset is a subset of the first training data set and comprises a pure data set, and the sample images are digestive tract endoscope images. According to the digestive tract endoscope image processing method, the training data volume is increased, and meanwhile, the influence of label labeling errors on the model prediction accuracy can be reduced.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a method, device, storage medium, equipment and system for processing gastrointestinal endoscope images. Background technique [0002] As the core of artificial intelligence, computer vision technology and machine learning technology have been applied in various fields, such as the medical field. In the medical field, computer vision technology and machine learning technology are used to process medical images to identify whether a patient suffers from a certain disease. For example, machine learning models are used to assist doctors in the detection of digestive tract diseases. [0003] Related technologies When performing digestive tract disease detection through digestive tract endoscopic image processing, the usual practice is to obtain a training data set, which includes marked digestive tract endoscopic images (also called sample images); accordin...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides a method, device, storage medium, equipment and system for image processing of digestive tract endoscopy, which solves the problem of poor model accuracy in related technologies, which leads to poor detection accuracy in the image detection process

Images