Method and device for generating training sample, computer readable medium and electronic equipment

Abstract

The embodiment of the invention provides a method and device for generating a training sample, a computer readable medium and electronic equipment. The method comprises the steps of acquiring a medical image set to be labeled, and respectively determining a first sampling proportion and a second sampling proportion for image layer thicknesses in an early image set and a later image in the medical image set, so as to obtain an early sampling image and a later sampling image through sampling; and comparing the image information of the target area in the early sampling image with the image information of the target area in the later sampling image, and determining a label representing the change trend of the target area in the early sampling image based on a generated comparison result. According to the technical scheme, on the premise that the image quality is guaranteed, the number of the to-be-labeled images is reduced, the label of the early sampling image is generated according to the comparison result of the corresponding labeled image information, and on the basis that the medical image labeling accuracy is guaranteed, the medical image labeling efficiency is improved, and the labeling cost is reduced.

Description

technical field [0001] The present application relates to the field of computer technology, in particular, to a method, device, computer-readable medium and electronic equipment for generating training samples. Background technique [0002] With the development of artificial intelligence technology becoming more and more extensive and exquisite, more and more application environments use artificial intelligence technology to solve corresponding technical problems, such as three-dimensional object reconstruction, face recognition and so on. In the related art, these problems are generally dealt with by constructing a model. However, the construction of the model depends on a large number of data samples, and each sample needs to have its corresponding label in order to make the trained model more accurate. [0003] In practical applications, each sample is basically labeled manually. This method relies on too many human resources and consumes costs, especially when the numbe...

AI Technical Summary

Problems solved by technology

[0003] In practical applications, each sample is basically labeled manually. This method relies on too many human resources and consumes costs, especially when the number of samples required for training the model is large. This method of sample labeling often consumes a lot of cost, and the efficiency is too low, which affects the construction efficiency and cost of the entire model.

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