Interpretable detector for cervical cytology using self-attention and cell origin group guidance

Deep learning has advanced the development of automated cervical cytology, yet limited studies have delved into methods for incorporating medical domain knowledge, and model interpretability has not been thoroughly investigated. To address this issue, this paper proposes a novel, explainable detection method for abnormal cervical cells, called dual -stream self -attention based feature fusion and origin grouping network (DSAFFOGNet). To encourage the model to focus more on lesion cells and cell nuclei of diagnostic significance, the dual -stream self -attention (DSA) module is introduced to enhance the learning of lesion -specific features. In view of the complex background, cell dense distribution, cell overlap, or clumps existing in the actual cervical cytology images, multi -scale features are extracted and fused by using the path aggregation network (PAN) to enhance the feature representation ability. By integrating biomedical insights regarding cell provenance and formulating an origin grouping loss, DSA-FFOGNet adjusts the penalties for cervical cells originating from different groups, thereby enhancing the optimization of the model training process. To further improve the detection performance, the classification and localization tasks are decoupled via the use of double detection heads. Extensive experiments validate the robustness of the proposed DSA-FFOGNet. The visualization of class activation maps (CAMs) showcases the model's interpretability. The proposed approach advances the application and development of explainable artificial intelligence (XAI) models in cervical cytology and inspires further research in automated cervical cytology.