摘要目的:本文采用基于深度卷积神经网络的方法，针对人工智能在宫颈液基细胞片病理图像自动筛查中的应用价值，开展多中心的实际应用研究，并与细胞学医师的诊断进行比较及分析。方法:采用深度分割网络提取5 516张细胞学病理图像中的感兴趣区域618 333个，结合医师的经验训练出具有分析能力的深度分类网络，利用其分类结果构建特征，使用决策模型完成细胞病理图像的分级。结果:该方法对4 908例宫颈液基细胞片进行病理图像自动筛查，灵敏度为89.72%，特异度为58.48%，阳性预测值为33.95%，阴性预测率为95.94%。在4种不同制片或染色方法的细胞片中，本算法对于巴氏染色自然沉降片效果最佳，灵敏度为91.10%，特异度为69.32%，阳性预测值为41.41%，阴性预测值为97.03%。结论:深度卷积神经网络图像识别技术可初步应用于宫颈细胞学筛查。

abstractsObjective:To propose a method of cervical cytology screening based on deep convolutional neural network and compare it with the diagnosis of cytologists.Method:The deep segmentation network was used to extract 618 333 regions of interest (ROI) from 5, 516 cytological pathological images. Combined with the experience of physicians, the deep classification network with the ability to analyze ROI was trained. The classification results were used to construct features, and the decision model was used to complete the classification of cytopathological images.Results:The sensitivity and specificity were 89.72%, 58.48%, 33.95% and 95.94% respectively. Among the smears derived from four different preparation methods, this algorithm had the best effect on natural fallout with a sensitivity of 91.10%, specificity of 69.32%, positive predictive rate of 41.41%, and negative predictive rate of 97.03%.Conclusion:Deep convolutional neural network image recognition technology can be applied to cervical cytology screening.