A Multi-Task Feature Fusion Model for Cervical Cell Classification
Jian Qin, Yongjun He, Jinping Ge, Yiqin Liang
Abstract
Cervical cell classification is a crucial technique for automatic screening of cervical cancer. Although deep learning has greatly improved the accuracy of cell classification, the performance still cannot meet the needs of practical applications. To solve this problem, we propose a multi-task feature fusion model that consists of one auxiliary task of manual feature fitting and two main classification tasks. The auxiliary task enhances the main tasks in a manner of low-layer feature fusion. The main tasks, i.e., a 2-class classification task and a 5-class classification task, are learned together to realize their mutual reinforcement and alleviate the influence of unreliable labels. In addition, a label smoothing method based on cell category similarity is designed to bring inter-cell class information into the label. Comparative experimental results with other state-of-the-art models on the HUSTC and SIPaKMeD datasets prove the effectiveness of the proposed method. With a high sensitivity of 99.82% and a specificity of 98.12% for the 2-class classification task on the HUSTC dataset, our method shows potential to reduce cytologist workload.