A Cervical Cell Classification Framework Based on Multiview Supervised Contrastive Learning

Objectives: Cervical cell classification is fundamental for early cervical cancer detection. Deep convolutional neural networks (CNNs) have made great progress in enhancing the performance of cervical cell classification. However, most current methods have overlooked two major concerns: the pattern variations in cervical cells caused by data acquisition process and the misclassification of cervical cells with similar pathological properties. To address these issues, we develop a new cervical cell classification framework that incorporates supervised contrastive learning with CNN. Methods: To simulate the pattern variations of cervical cells, we first adopt data augmentation to generate multiple views of cell images, which are then fed into three main components of the model, including the encoder, contrastive, and classification modules. Moreover, we design a hybrid loss to jointly train the model to learn more robust cell representations by introducing the supervised contrastive loss into the traditional classification loss. Findings: Experimental results on four cell image datasets demonstrate that the proposed method achieves better performance than the competing methods. Our hybrid loss yields the highest F-score, improving the classification and supervised contrastive losses by 3.3% and 2.6%, respectively, further illustrating the superiority of our method in cervical cell classification. Novelty: Through the combination of supervised contrastive learning and traditional classification, our method obtains better representations from cervical cell images, enhancing the model robustness.