Method and apparatus for acquiring a cell classification model

By training a cell classification model and optimizing feature map weights, the problem of classifying objects with small inter-class differences was solved, achieving fast and accurate automated classification and improving the efficiency and accuracy of white blood cell classification.

CN114495096BActive Publication Date: 2026-06-16XIN HUA HOSPITAL AFFILIATED TO SHANGHAI JIAO TONG UNIV SCHOOL OF MEDICINE
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
XIN HUA HOSPITAL AFFILIATED TO SHANGHAI JIAO TONG UNIV SCHOOL OF MEDICINE
Filing Date
2020-10-23
Publication Date
2026-06-16

AI Technical Summary

⚠Technical Problem

Existing classification methods are difficult to effectively distinguish objects with small differences between them, resulting in low efficiency and accuracy of manual classification. In particular, in the classification of white blood cells, relying on manual microscopic examination leads to a large workload and is prone to errors.

⚗Method used

A pre-trained cell classification model is used, and feature maps are extracted and weighted through a convolutional neural network. An iterative algorithm is then used to optimize the weight distribution of the feature maps, thereby improving the feature discrimination ability and obtaining the optimal cell classification model.

🎯Benefits of technology

It enables rapid and accurate classification of objects with small inter-class differences, improving classification efficiency and accuracy while reducing manual workload and error rate.

✦ Generated by Eureka AI based on patent content.

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Abstract

The embodiment of the application discloses a method and device for obtaining a cell classification model, the method comprising: obtaining a trained cell classification model; obtaining M feature maps of a target to be classified according to test data and the cell classification model; M is a positive integer greater than or equal to 1; the test data comprises one or more targets to be classified; obtaining the weights of the M feature maps; inputting the M feature maps with weights into the cell classification model, optimizing the cell classification model, and thus obtaining an optimal cell classification model. Through the embodiment, the classification of a target with a small inter-class gap is realized, and the classification efficiency and accuracy are improved.
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