Improved YOLOv5s based method for immunohistochemically positive cell counting
10.3969/j.issn.1005-202X.2025.02.005
- VernacularTitle:基于改进YOLOv5s的免疫组化阳性细胞计数方法
- Author:
Xingyue CHEN
1
;
Ziyan JIA
;
Qing LI
;
Dachuan ZHANG
;
Lingjiao PAN
;
Dawei SHEN
Author Information
1. 江苏理工学院电气信息工程学院,江苏 常州 213001
- Publication Type:Journal Article
- Keywords:
positive cell;
target detection;
YOLOv5s;
immunohistochemistry;
survival prediction
- From:
Chinese Journal of Medical Physics
2025;42(2):167-174
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a novel method for immunohistochemically positive cell counting based on the improved YOLOv5s.Methods Regarding the small target characteristics of positive cells,a small target detection layer was added to refine feature extraction.Then,a bidirectional weighted feature pyramid network was used to replace path aggregation network(PANet)in the neck network for realizing multi-scale feature fusion.Additionally,the method used coordinate attention mechanism to make the model pay more attention to small target characteristics,and replaced the original GIoU with EIoU loss function for enhancing the detection performance.Results The model was trained on the self-built immunohistochemical image dataset.The average accuracy of the improved model was 89.3%,which was 4.0%higher than the original model and surpassed mainstream target detection models.The 5-year survival prediction model constructed with the method achieved an average accuracy of 76.8%and an average area under the curve of 0.81,demonstrating its superior prediction ability.Conclusion The proposed model can quickly detect the number of immunohistochemically positive cells and effectively assist doctors in survival prediction.
