基于稀疏自注意力的免疫组化图像蛋白质亚细胞定位OA

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.