A Bottom-Up Classification Method of Parasite Species Developmental Stages and Image Pixels

A developmental stage, bottom-up technology, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as insurmountable long tail problem, contribution weight distinction, poor detection effect, etc., to reduce labor costs and regional Medical disparities, suppression numbers, effects of improving parasite detection

Active Publication Date: 2021-11-05
BEIJING XIAOYING TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] Due to the long tail phenomenon of the parasite distribution, the loss function cannot distinguish the contribution weights of the samples of the tail category and the samples of the head category to the update of the entire model, and the simple samples and the difficult samples have the same loss function. Therefore, the biggest problem in parasite detection and classification is the long-tail problem, that is, it is easy to miss the detection of parasites, the detection effect is not good, and it is not practical

Method used

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  • A Bottom-Up Classification Method of Parasite Species Developmental Stages and Image Pixels
  • A Bottom-Up Classification Method of Parasite Species Developmental Stages and Image Pixels
  • A Bottom-Up Classification Method of Parasite Species Developmental Stages and Image Pixels

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Experimental program
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Effect test

Embodiment 1

[0094] Such as figure 1 As shown, a bottom-up parasite species development stage and image pixel classification method includes the following steps:

[0095]S1. Preparation of training set and test set: under the microscope imaging equipment, the blood slides for labeling are seamlessly photographed and collected by field of view, and the border parts of the images are seamlessly connected by stitching adjacent images to obtain the image set for labeling , mark the position, category, developmental stage and pixel point category of the parasite on a single image in the image set for labeling, generate labeling data, and divide the labeling data into a training set and a test set;

[0096] S2. Construction of Transformer-based blood parasite classification, detection and segmentation model: build a Transformer-based blood parasite classification, detection and segmentation model, use a loss function to optimize the model to suppress the long-tail distribution, use the training...

Embodiment 2

[0100] Such as figure 1 As shown, a bottom-up parasite species development stage and image pixel classification method includes the following steps:

[0101] S1. Preparation of training set and test set: under the microscope imaging equipment, the blood slides for labeling are seamlessly photographed and collected by field of view, and the border parts of the images are seamlessly connected by stitching adjacent images to obtain the image set for labeling , mark the position, category, developmental stage and pixel point category of the parasite on a single image in the image set for labeling, generate labeling data, and divide the labeling data into a training set and a test set;

[0102] S2. Construction of Transformer-based blood parasite classification, detection and segmentation model: build a Transformer-based blood parasite classification, detection and segmentation model, use a loss function to optimize the model to suppress the long-tail distribution, use the trainin...

Embodiment 3

[0133] Such as image 3 As shown, a bottom-up parasite species development stage and image pixel classification method includes the following steps:

[0134] 1. Whole film collection and splicing

[0135] In order to avoid the missed detection of parasites, the present invention first seamlessly takes pictures and collects the blood slides field by field under the microscopic imaging equipment, and for the image boundary, adopts the mode of adjacent image splicing to seamlessly connect the boundary parts, so as to avoid Leave out any possible parasites.

[0136] 2. Parasite data labeling

[0137] Professional doctors use specific labeling tools to mark the location of parasites, category information and pixel category information on a single field of view. After the amount of labeling reaches a certain scale, the labeling data is divided into a training set and a test set according to a certain ratio. Learn to prepare data for network models.

[0138] 3. Construction of Tr...

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Abstract

The present invention provides a bottom-up parasite species development stage and image pixel classification method, using a microscope or an automatic slide scanning device to digitize the slide, constructing a Transformer-based deep learning algorithm to detect parasites visually by field of view, using The focal_loss function, the CB_focal_loss function and the Jaccard function suppress long-tailed distributions. The method of the present invention is simple, and only a thin blood film can be used for parasite identification, and the detection time and labor cost can also be reduced, the detection level of parasites in local areas can be improved, and the parasite species and development stages can be identified, and the efficiency is high. High, fast, reduce labor costs and regional medical differences.

Description

technical field [0001] The invention relates to the technical fields of calculation, calculation and counting, in particular to a bottom-up method for classifying the developmental stages of parasite species and image pixels. Background technique [0002] Parasite detection is the main means of public prevention and control in hospitals and CDCs. Common parasites include Plasmodium, Amoeba, Leishmania donovani, Toxoplasma gondii, Babesia canis, Trypanosoma evanii, Carl's living leukocytozoa and so on. Flow cytometry screens for possible presence, not detection of parasites. [0003] At present, the detection methods of parasites mainly include: [0004] (1) Microscopic examination: Doctors use a microscope to observe peripheral blood smears to check for malaria parasites is a routine method for diagnosing malaria. Usually, it is necessary to observe the entire blood slide under a microscope with a 100x objective lens. Generally, thousands of fields of view need to be check...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06K9/38G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06N3/08G06T2207/10056G06T2207/20081G06T2207/20084G06T2207/30004G06V10/28G06N3/045G06F18/241Y02A50/30
Inventor 李柏蕤连荷清
Owner BEIJING XIAOYING TECH CO LTD
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