A Deep Learning-Based Method for Detecting Giant Cells in Ovarian Cancer Polyploid Tumors from H&E Images

By constructing the OCDet model, the subjectivity and complexity of detecting polyploid tumor giant cells in H&E staining images were resolved, achieving efficient and accurate automated detection. This model is applicable to the detection of polyploid tumor giant cells in ovarian cancer, improving the efficiency and accuracy of pathological diagnosis.

CN119151873BActive Publication Date: 2026-07-24TSINGHUA SHENZHEN INTERNATIONAL GRADUATE SCHOOL
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TSINGHUA SHENZHEN INTERNATIONAL GRADUATE SCHOOL
Filing Date
2024-08-21
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies for detecting ovarian cancer polyploid tumor giant cells in H&E-stained images suffer from high subjectivity, complex operation, and long processing time. Furthermore, deep learning models lack sufficient accuracy and stability in heterogeneous and small sample sizes.

Method used

An OCDet model was constructed, using CSPDarkNet as the feature extraction backbone and combining it with the attention mechanism ECA. The ECA-RER module enhances salient features and removes redundant features, while the ECA-MREF module performs multi-scale feature fusion. The loss function is optimized using the detection head, achieving efficient and accurate detection of polyploid tumor giant cells.

Benefits of technology

It enables rapid, accurate, and automated detection of polyploid tumor giant cells in H&E stained images, improving the efficiency of pathological diagnosis, reducing human error, and has good scalability and customizability, making it suitable for cell detection under different disease types and staining conditions.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN119151873B_ABST
    Figure CN119151873B_ABST
Patent Text Reader

Abstract

A kind of H&E image ovarian cancer polyploid giant cell (PGCCs) detection method based on deep learning, including constructing specific data set, image annotation and data division, using OCDet model to carry out feature learning and optimization, and model training and evaluation.The method first establishes the H&E staining image data set containing PGCCs, then accurately labels the image and divides it into training, verification and test set.OCDet model takes CSPDarkNet as the core, combines ECA mechanism, focuses on the deep learning and re-encoding of pathological semantic features.Through the training data set, the model updates parameters through back propagation and gradient descent, optimizes to identify PGCCs features.The verification set is used for model tuning, and the test set is used for evaluating the performance of the model.The automatic detection technology of the application can help doctors improve the diagnosis efficiency and reduce the error, provide an important reference for clinical treatment and prognosis evaluation, and show significant clinical application value.
Need to check novelty before this filing date? Find Prior Art