Method and device for training machine learning model based on endoscopic image, and storage medium

Abstract

The invention provides a method and device for training a machine learning model. The method comprises the following steps: a first stage: inputting an unlabeled sample set, selecting a to-be-labeledsample from the unlabeled sample set through active learning based on the initialized or pre-trained machine learning model, labeling the sample to be labeled, and storing the labeled sample in a labeling data set, dividing the annotation data set into a training data set and a verification data set, training the machine learning model by using the training data set to obtain a trained machine learning model, and verifying the trained machine learning model by using the verification data set to obtain the performance of the trained machine learning model; and a second stage: repeating the steps in the first stage when the performance of the trained machine learning model is less than a predetermined performance index, until the performance of the trained machine learning model is greater than or equal to a predetermined performance index.

Description

technical field [0001] The present invention relates to the field of machine learning, in particular to a method, device and storage medium for training a machine learning model based on endoscopic images. Background technique [0002] Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the nature of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making. [0...

AI Technical Summary

Problems solved by technology

In addition to the above shortcomings, most of these labels need to be completed by doctors

However, different doctors may draw inconsistent labeling conclusions due to their respective professional knowledge, work experience, work status, etc., which may affect the quality of the training data set, thereby affecting the accuracy and robustness of the trained model

Images