Detection and identification method for spores in gynecological micro-ecology, equipment and storage medium

Abstract

The invention relates to a detection and identification method for spores in gynecological micro-ecology, equipment and a storage medium, and the method comprises the following steps: inputting a to-be-detected image into an effective target AI detection model, detecting a corresponding target object, and secondarily determining whether the target object is a spore through a machine vision technology. The effective target detection AI model generation method comprises the following steps: collecting a multi-scene microscopic image; carrying out manual category labeling according to a conventional method; expanding the labeling area of the epithelial cell nucleus by introducing hyper-parameters; and making an effective training set with a large inter-class gap and a small intra-class gap, and inputting the effective training set into a convolutional neural network for training based on a backbone network architecture and in combination with a deep learning target detection framework to obtain an effective target AI detection model. According to the method, an effective target detection AI model can be trained by introducing a hyper-parameter to expand a labeling area of an epithelial cell nucleus; Whether the target object is the spore or not is secondarily determined through a machine vision technology, so that the false detection rate and the omission ratio of the spore are effectively reduced.

Description

technical field [0001] The invention relates to the field of intelligent detection and identification of spores in microscopic images of gynecological reproductive tract microecology, in particular to an intelligent detection and identification method, equipment and storage medium of spores in gynecological microecology. Background technique [0002] Under certain conditions, Candida invades the female lower genital tract and causes inflammation of the vulvar skin and mucous membranes. It is often called vulvovaginal pseudomycosis, also known as fungal vaginitis or fungal vaginitis, and spores are one of them. A common pathogenic fungus of the female genital tract. At present, the Gram staining method for biological specimens of female reproductive tract secretion is the most commonly used test method, and the morphological examination under the microscope is the gold standard for gynecological vaginal microecological diagnosis. It plays a crucial role in diagnosing female ...

AI Technical Summary

Problems solved by technology

For epithelial cell nuclei that are very similar to spores, the currently commonly used labeling rule is to mark according to the smallest circumscribed upright rectangle of the epithelial cell nucleus, but the areas of different epithelial cell nuclei are different in size and have a large gap, among which the area is small and the Gram stain is deeply stained The morphology of epithelial nuclei and spores is very similar. Therefore, in the prior art, marking epithelial nuclei and spores according to the conventional minimum circumscribed upright rectangle may easily cause large intra-class gaps between epithelial cell nuclei and small inter-class gaps between epithelial cell nuclei and spores. It is very good to train an effective target detection model, even if combined with traditional computer vision technology, it is easy to cause false detection of spores

In addition, for the exfoliated white blood cell nuclei and sperm, because the amount of such data is very small, it is difficult to collect a large amount of data. Therefore, in the case of insufficient data, it is impossible to use deep learning technology to perform a good comparison with the spores. distinguish

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