Full-piece bone marrow cell automatic counting method and system based on visual large model and self-supervised learning

By using visual big model and self-supervised learning technology in the automatic bone marrow cell counting system, the automated classification and counting of bone marrow cells in the whole piece is solved, and the problems of labor-intensive, subjective and generalized in the existing technology are improved, and the accuracy and adaptability of the system are improved.

CN119763108AActive Publication Date: 2025-04-04CHONGQING UNIV
8 Cites 0 Cited by

Patent Information

Application Number
CN202411760627.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-03
Publication Date
2025-04-04
Estimated Expiration
2044-12-03

AI Technical Summary

Technical Problem

The prior art has labor-intensive and subjective problems in the classification and counting of bone marrow aspiration smear cells, and deep learning-based methods require a large amount of labeled data and computing resources, which have poor generalization and are difficult to adapt to the staining differences and ROI segmentation effects of different medical centers.

Method used

The full-piece bone marrow cell automatic counting method based on visual big model and self-supervised learning is adopted. Through the ROI segmentation model, TOI classification model, cell segmentation model and cell classification model, the entire process is automated cell classification and counting to reduce the impact of staining differences and texture differences.

Benefits of technology

It improves the accuracy and efficiency of cell classification counting, enhances the generalization ability and adaptability of the system, and can provide high-quality cell classification results in the environment of different medical centers.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides a full bone marrow cell automatic counting method and system based on a visual large model and self-supervised learning. The full bone marrow cell automatic counting method comprises the following steps: segmenting a region of interest from a bone marrow smear by adopting an RO I segmentation model; cutting out slices from the bone marrow smear corresponding to the region of interest by adopting a TO I classification model, and carrying out binary classification to select interested slices; segmenting all cells on the interested slice by adopting a cell segmentation model based on a visual large model; and classifying the cells by adopting a cell classification model based on self-supervised learning, and finally counting. The method has strong generalization and high accuracy, and can provide valuable reference for pathologists of various medical centers.
Need to check novelty before this filing date? Find Prior Art