现代信息科技2026，Vol.10Issue(10)：146-149,4.DOI:10.19850/j.cnki.2096-4706.2026.10.027
基于稀疏自注意力的免疫组化图像蛋白质亚细胞定位
Protein Subcellular Localization in Immunohistochemistry Images Based on Sparse Self-Attention
乔阳 1肖瑞希 1洪勇辉1
作者信息
- 1. 江西科技师范大学 信息工程学院,江西 南昌 330038
- 折叠
摘要
Abstract
To address the challenges of high computational burden and loss of fine-grained details when processing high-resolution Immunohistochemistry(IHC)images for Protein Subcellular Localization(PSL)prediction,this paper proposes a multi-label prediction model based on a Sparse Self-Attention mechanism,named SSA-PLoc.The model employs a hierarchical Sparse Self-Attention encoding architecture.By setting different sparsity rates,it computes Self-Attention within local windows,which effectively reduces computational complexity and focuses on key subcellular structures.Experimental results on the Vislocas dataset demonstrate that the proposed model achieves a subset accuracy of 61.29%with full-resolution input and attains competitive performance across multiple PSL evaluation metrics,providing a feasible solution for accurate protein subcellular localization analysis from large-scale biological images.关键词
蛋白质亚细胞定位/免疫组织化学图像/稀疏自注意力/多标签分类Key words
protein subcellular localization/immunohistochemistry image/Sparse Self-Attention/multi-label classification分类
信息技术与安全科学引用本文复制引用
乔阳,肖瑞希,洪勇辉..基于稀疏自注意力的免疫组化图像蛋白质亚细胞定位[J].现代信息科技,2026,10(10):146-149,4.
