Endoscope image detection method, device, storage medium and electronic equipment

Abstract

The invention relates to an endoscope image detection method, a device, a storage medium and electronic equipment, and aims to reduce manpower and time for model deployment in an endoscope image detection scene, improve model deployment efficiency and model iteration efficiency and improve endoscope image detection efficiency. The method comprises the following steps: acquiring an endoscope image to be detected; executing a plurality of target tasks on the endoscope image through an endoscope image detection model to obtain a plurality of task detection results corresponding to the endoscope image, the endoscope image detection model being used for executing the plurality of target tasks through the following steps: extracting image features corresponding to the target tasks from the endoscope image through a task feature network, and performing fusion calculation on each extracted image feature and a pre-trained quality control image feature through an interactive feature network to obtain a fusion feature corresponding to each target task, and for each target task, determining a task detection result corresponding to the target task through a target task network according to the fusion feature corresponding to the target task.

Description

Technical field [0001] The present disclosure relates to the field of medical image, and in particular, to an endoscope image detecting method, device, storage medium, and electronic device. Background technique [0002] With the continuous development of artificial intelligence technology, endoscopic, such as colonoscopy, or rectal mirrors, can have different detection tasks through different AI (Artificial Intelligence, artificial intelligence) models, and each test task includes an AI model plus A quality control model, so a plurality of models need to be deployed in practical applications to meet multi-tasking requirements. If there is a new detection task, you need to deploy the AI ​​models and corresponding quality control models for performing new detection tasks. If the new detection task is more, the number of models that need to be deployed will be multiplied, which increases the workload used for model deployment. And during the model iteration, since each independent ...

AI Technical Summary

Problems solved by technology

If there are many new detection tasks, the number of models to be deployed will increase exponentially, increasing the workload for model deployment

And during the model iteration process, since each independent model needs to be iterated, as the number of models increases, the overall iteration efficiency of the model will be affected, which will affect the endoscopic image detection efficiency

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