Medical image sample screening method and device, computer equipment and storage medium

Abstract

The invention relates to a medical image sample screening method and device, computer equipment and a storage medium, for carrying out intelligent screening on unlabeled medical image samples. The medical image sample screening method includes the steps: carrying out model training on a labeled sample set by utilizing a Mask-RCNN model so as to obtain a focus target detection depth model; predicting the unlabeled medical image sample set according to the focus target detection depth model to obtain a prediction result of each medical image sample and judge a labeling value; and selecting a medical image sample with high annotation value to perform annotation confirmation, performing iterative updating on the focus target detection depth model, and ending the iterative updating until the performance of the focus target detection depth model cannot continue to annotate a new sample. According to the medical image sample screening method, under the condition of limited computing resourcesor annotation cost, the high-value small data set is actively mined and extracted, and efficient diagnosis and decision making are achieved by simulating a medical expert intelligent learning mode, and the intelligent degree is high, and the processing speed is high, and the problem that the annotation efficiency is low is effectively solved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a medical image sample screening method, device, computer equipment and storage medium. Background technique [0002] The detection of lesions, key organs and other targets based on medical images is one of the most frequently used tasks in the field of artificial intelligence-assisted diagnosis and treatment. The actual medical image data collected clinically has complex semantics and target layout. The occlusion between objects makes accurate and effective object detection in medical imaging extremely difficult. [0003] At present, the supervised learning algorithm based on deep learning has achieved certain results in many computer vision application fields. It needs to be based on a large number of labeled training samples. Good feature and detection models require a large number of labeled samples. When applied to medical imaging for lesion target detection, the a...

AI Technical Summary

Problems solved by technology

[0006] Based on this, it is necessary to solve the problem of low efficiency and low accuracy of the model for intelligently screening samples during supervised learning of medical image sample detection. Valuable medical image samples are used to iteratively improve the model to further improve the level of the model's intelligent labeling of lesion targets

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