Cervical cancer is one of the most frequent cancers in women worldwide, yet the early detection and treatment of lesions via regular cervical screening have led to a drastic reduction in the mortality rate. However, the routine examination of screening as a regular health checkup of women is characterized as time-consuming and labor-intensive, while there is lack of characteristic phenotypic profile and quantitative analysis. In this research, over the analysis of a privately collected and manually annotated dataset of 130 cytological whole-slide images, the authors proposed a deep-learning diagnostic system to localize, grade, and quantify squamous cell abnormalities. The system can distinguish abnormalities at the morphology level, namely atypical squamous cells of undetermined significance, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, and squamous cell carcinoma, as well as differential phenotypes of normal cells. The case study covered 51 positive and 79 negative digital gynecologic cytology slides collected from 2016 to 2018. Our automatic diagnostic system demonstrated its sensitivity of 100% at slide-level abnormality prediction, with the confirmation with three pathologists who performed slide-level diagnosis and training sample annotations. In the cellular-level classification, we yielded an accuracy of 94.5% in the binary classification between normality and abnormality, and the AUC was above 85% for each subtype of epithelial abnormality. Although the final confirmation from pathologists is often a must, empirically, computer-aided methods are capable of the effective extraction, interpretation, and quantification of morphological features, while also making it more objective and reproducible.

宫颈癌是全世界女性最常见的癌症之一，但通过定期宫颈筛查及早发现和治疗病变，可大幅降低死亡率。然而，常规筛查作为女性定期健康检查的特点是费时费力，且缺乏特征表型特征和定量分析。在这项研究中，通过对私人收集并手动注释的 130 个细胞学全切片图像数据集进行分析，作者提出了一种深度学习诊断系统来定位、分级和量化鳞状细胞异常。该系统可以区分形态水平的异常，即意义未明的非典型鳞状细胞、低度鳞状上皮内病变、高级鳞状上皮内病变和鳞状细胞癌，以及正常细胞的差异表型。该案例研究涵盖了2016年至2018年收集的51张阳性和79张阴性数字妇科细胞学切片。我们的自动诊断系统在切片水平异常预测方面表现出100%的敏感性，并得到了进行切片水平诊断和培训的三位病理学家的确认示例注释。在细胞水平分类中，我们在正常和异常之间的二元分类中获得了 94.5% 的准确率，并且上皮异常每种亚型的 AUC 都在 85% 以上。虽然病理学家的最终确认往往是必须的，但根据经验，计算机辅助方法能够有效地提取、解释、