一种融合个性化细胞关系和背景信息的宫颈细胞分类方法

Cervical cell classification plays a crucial role in assisting the diagnosis of cervical cancer. However, existing methods for cervical cell classification do not enough consider relationships among cells and background information, and fail to effectively simulate the diagnostic approach of pathology doctors. As a result, their classification performance is limited. In this study, a novel approach that integrates cell relationships and background information for cervical cell classification is proposed. The proposed method consists of a Graph Attention Branching for Cell-Cell Relationships (GAB-CCR) and a Background Attention Branching for Whole Slide Images (BAB-WSI). GAB-CCR utilizes cosine similarity of cell features to construct preliminary graphs representing similar and distinct cell relationships. Additionally, GAB-CCR enhances the ability of models in modeling cell relationships through GATv2. BAB-WSI employs multi-head attention to effectively capture crucial information on the slide background and reflect the importance of different regions. Finally, the enhanced cell and background features are fused to improve the classification performance of the network. Experimental results demonstrate that the proposed method achieves significant improvements over the baseline model, Swin Transformer-L, with improvement in accuracy, sensitivity, specificity, and F1-Score by 15.9%, 30.32%, 8.11%, and 31.62% respectively.