Pathological slice training image set acquisition method, system and device and medium

Abstract

The invention discloses a pathological section training image set acquisition method, which comprises the following steps: acquiring a target pathological section, acquiring at least one visual fieldimage of the target pathological section, each visual field image comprising at least one currently labeled cell; acquiring cell labeling information labeled by at least one currently labeled cell inthe at least one view image; obtaining a to-be-screened training image set according to the at least one view image and the cell labeling information labeled by the at least one currently labeled cellin the at least one view image; and acquiring an image evaluation standard, and performing image evaluation on each view image in the to-be-screened training image set according to the image evaluation standard so as to screen and obtain the training image set meeting the image evaluation standard. According to the method, a large amount of invalid content contained in the image training set is removed, memory occupation of the image training set can be effectively reduced, and the method is very suitable for obtaining the data source of the artificial intelligence model. In addition, the invention also provides a training image set acquisition system, equipment and a medium.

Description

technical field [0001] The invention relates to the technical field of digital pathology, in particular to a method, system, device and medium for acquiring a training image set of pathological slices. Background technique [0002] For bone marrow pathology, blood pathology, thyroid cell pathology, etc., it is generally necessary to smear the samples and then stain them and make pathological sections for easy labeling and observation. However, because the entire area of ​​the pathological section is relatively large, and the generated image files are often very large, even up to tens of gigabytes in size. However, in these dozens of G image files, the effective cell image area is often relatively small, which will cause a huge waste of data storage, transmission and calculation. However, if the artificial intelligence model is trained based on such a huge image file, the memory occupied will be huge when building the artificial intelligence database, and there will be a lot...

AI Technical Summary

Problems solved by technology

However, in these dozens of G image files, the effective cell image area is often relatively small, which will cause a huge waste of data storage, transmission and calculation.

However, if the artificial intelligence model is trained based on such a huge image file, the memory occupied will be huge when building the artificial intelligence database, and there will be a lot of effective content to be trained, which will cause a considerable burden on cost expenditure.

Images