Instance segmentation model sample screening method and device, computer equipment and medium

Abstract

The invention relates to artificial intelligence, can be used for medical image analysis auxiliary scenes, and provides an instance segmentation model sample screening method. The method comprises thesteps: reading an original data set, selecting a first to-be-labeled sample of which the information amount is greater than that of remaining samples from an unlabeled set based on an active learningmode, obtaining a first annotation set in a mode of manually annotating a plurality of first to-be-annotated samples; selecting a second to-be-labeled sample of which the confidence is higher than aset value from all the remaining samples based on a semi-supervised learning mode, obtaining a second labeling set in a mode of pseudo labeling of the second to-be-labeled sample, and taking the firstlabeling set, the second labeling set and the labeled set as a training set together. According to the method, a large number of samples used for training the image instance segmentation model can beobtained while the manual annotation amount of the samples is reduced, and then the more ideal instance segmentation model accuracy can be achieved. In addition, the invention also relates to a blockchain technology, and both the original data set and the training set can be stored in the blockchain.

Description

technical field [0001] The invention relates to the field of artificial intelligence technology, and can be applied in the field of image instance segmentation. The invention specifically provides an instance segmentation model sample screening method, device, computer equipment and media. Background technique [0002] With the continuous development of deep learning, computer vision has achieved more and more success, thanks to the support of large training data sets. The training data set (referred to as the training set) is a data set with rich label information. Collecting and labeling such a data set usually requires a huge human cost. [0003] Compared with image classification technology, image instance segmentation has a higher degree of difficulty, and a large amount of labeled training data is required to truly realize the instance segmentation function. However, the number of labeled samples available is often insufficient relative to the size of the problem, or ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems existing in the prior art that it is difficult to obtain a large number of samples for image instance segmentation model training, the present invention can provide an instance segmentation model sample screening method, device, computer equipment and media, which can reduce the amount of manual labeling of samples The purpose of obtaining a large number of samples at the same time

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