Abstract
Cervical cancer is one of the most prevalent cancers among women, posing a significant threat to their health. Early screening can detect cervical precancerous lesions in a timely manner, thereby enabling the prevention or treatment of the disease. The use of pathological image analysis technology to automatically interpret cells in pathological slices is a hot topic in digital medicine research, as it can reduce the substantial effort required from pathologists to identify cells and can improve diagnostic efficiency and accuracy. Therefore, we propose a cervical cell detection network based on collecting prior knowledge and correcting confusing labels, called PGCC-Net. Specifically, we utilize clinical prior knowledge to break down the detection task into multiple sub-tasks for cell grouping detection, aiming to more effectively learn the specific structure of cells. Subsequently, we merge region proposals from grouping detection to achieve refined detection. In addition, according to the Bethesda system, clinical definitions among various categories of abnormal cervical cells are complex, and their boundaries are ambiguous. Differences in assessment criteria among pathologists result in ambiguously labeled cells, which poses a significant challenge for deep learning networks. To address this issue, we perform a labels correction module with feature similarity by constructing feature centers for typical cells in each category. Then, cells that are easily confused are mapped with these feature centers in order to update cells' annotations. Accurate cell labeling greatly aids the classification head of the detection network. We conducted experimental validation on a public dataset of 7410 images and a private dataset of 13,526 images. The results indicate that our model outperforms the state-of-the-art cervical cell detection methods.