The global elimination of cervical cancer is a key public health goal set by the World Health Organization (WHO), with screening programs reducing mortality by up to 80%. However, access to experts and biopsy services is limited in low- to middle-income countries (LMICs). Deep learning (DL)-based algorithms offer promising support for screening, but most existing approaches have been developed and validated on private datasets from single countries. We present the first DL-based approach to cervical cancer screening validated on data from multiple countries. Technically, we phrase the problem of detecting and classifying lesions in colposcopy images as a multi-task learning problem, in which we simultaneously perform image-level classification and lesion segmentation. Our model was trained on a private data set of acid stain colposcopy images with manually generated lesion segmentation masks and corresponding histopathological results, employing extensive data augmentation to address image variability. In an in-distribution validation with pathology results serving as ground truth, our algorithm outperformed medical experts (Balanced Accuracy: 0.68 vs 0.64) in CIN1- (Cervical intraepithelial neoplasia grade 1 or lower) versus CIN2+ (grade 2 or higher) classification. External validation on four colposcopy data sets from four countries featuring radical differences in prevalence and patient characteristics yielded superior performance of our method compared to baseline methods. Performance variability across countries was high with AUC values ranging from 0.54 - 0.80. Overall, algorithm performance varied with age, transformation zone (cervical area most prone to lesion development), presence of comorbidities and pathognomonic signs, with comorbidities having by far the largest negative effect. Future work should focus on improving model robustness and generalizability.

翻译：世界卫生组织（WHO）设定的全球消除宫颈癌是一个关键的公共卫生目标，筛查项目可将死亡率降低高达80%。然而，在中低收入国家（LMICs），专家和活检服务的可及性有限。基于深度学习（DL）的算法为筛查提供了有前景的支持，但大多数现有方法是在来自单一国家的私有数据集上开发和验证的。我们提出了首个基于深度学习的宫颈癌筛查方法，并在来自多个国家的数据上进行了验证。在技术上，我们将阴道镜图像中病变检测和分类的问题表述为多任务学习问题，同时执行图像级分类和病变分割。我们的模型在一个带有手动生成病变分割掩膜及相应组织病理学结果的醋酸染色阴道镜图像私有数据集上进行了训练，并采用广泛的数据增强技术来处理图像变异性。在以病理结果作为金标准的分布内验证中，我们的算法在CIN1-（宫颈上皮内瘤变1级或更低）与CIN2+（2级或更高）的分类上优于医学专家（平衡准确率：0.68 vs 0.64）。在对来自四个国家的四个阴道镜数据集进行外部验证时（这些数据集在患病率和患者特征上存在显著差异），我们的方法相比基线方法表现出优越性能。不同国家的性能变异性较高，AUC值范围从0.54到0.80。总体而言，算法性能随年龄、转化区（最易发生病变的宫颈区域）、合并症及病征的存在而变化，其中合并症的负面影响最大。未来工作应侧重于提高模型的鲁棒性和泛化能力。