Multi-Task Collaborative Assisted Training Method for Grouping Fuzzy Categories Classification of Cervical Cancer Cells

Cervical cancer is a malignant tumor that endangers women's life and health. While deep learning has enhanced the accuracy of cervical cell classification, there remain obstacles impeding further performance enhancement, including the similarities between different categories, variability between single cells and cell clusters, as well as the accuracy of annotations. To address these issues, a novel multi-task collaborative framework for cervical cell classification is proposed. Specifically, to solve the similarity between different categories, we propose a grouping cell contrast auxiliary branch, which divides cervical cells into different groups and utilizes supervised contrastive learning to learn representative feature between different categories. And we introduce a multi-level cell classification auxiliary branch that simultaneously performs 5-class, 3-class, and 2-class classification tasks, and explicitly constrains the inter-class relationship learning of cervical cells. Furthermore, to solve the variations within the same category of single cells and cell clusters, we propose an image reconstruction auxiliary branch, which encourages the model to learn more contextual features. Finally, to solve subjectivity and accuracy of annotations, we introduce a soft label distillation auxiliary branch, which constrains the consistency of probability distributions between the encoder and the momentum encoder. It is worth noting that these auxiliary branches only work during training and will not add additional computational consumption during inference. We validate on the HSJCC, DSCC and SIPaKMeD datasets. Compared to existing methods, our approach has achieved outstanding performance and effectively mitigates the issues raised, demonstrating its effectiveness in automated cervical cell classification.