Abstract

Info

Title:: Improved YOLOv5s based method for immunohistochemically positive cell counting

Author(s):: CHEN Xingyue1; JIA Ziyan1; LI Qing2; ZHANG Dachuan2; PAN Lingjiao1; SHEN Dawei1; 1. School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China 2. Department of Pathology, Changzhou First Peoples Hospital, Changzhou 213004, China

Keywords:: Keywords: positive cell target detection YOLOv5s immunohistochemistry survival prediction

Abstract:: 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.