PDF(Pubmed)
Abstract：
Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods.
摘要：
传统的宫颈癌诊断主要依靠人乳头瘤病毒(HPV)浓度检测。考虑到HPV浓度因个体而异，并随时间波动，此方法需要多个测试，导致高成本。最近,一些学者关注宫颈细胞学诊断的方法。然而,宫颈癌细胞具有复杂的结构特征和不同细胞亚型之间的微小差异，这给宫颈癌的高精度筛查带来了巨大的挑战。在本文中,提出了一种基于ConvNeXt的高精度宫颈癌癌前病变筛查分类方法,利用自监督数据增强和集成学习策略实现宫颈癌细胞特征提取和类间判别，分别。我们使用深宫颈细胞学水平（DCCL）数据集，其中包括来自32至67岁参与者的1167份宫颈细胞学标本,用于算法训练和验证.我们在DCCL数据集上测试了我们的方法，最终分类精度比以前的先进模型高8.85%，这意味着我们的方法与其他先进方法相比具有显著的优势。
