Computational cytology is a critical, rapid-developing, yet challenging topic
in the field of medical image computing which analyzes the digitized cytology
image by computer-aided technologies for cancer screening. Recently, an
increasing number of deep learning (DL) algorithms have made significant
progress in medical image analysis, leading to the boosting publications of
cytological studies. To investigate the advanced methods and comprehensive
applications, we survey more than 120 publications of DL-based cytology image
analysis in this article. We first introduce various deep learning methods,
including fully supervised, weakly supervised, unsupervised, and transfer
learning. Then, we systematically summarize the public datasets, evaluation
metrics, versatile cytology image analysis applications including
classification, detection, segmentation, and other related tasks. Finally, we
discuss current challenges and potential research directions of computational
cytology.

本文调查了 120 多篇基于深度学习的细胞学图像分析研究，介绍了全监督、弱监督、无监督和迁移学习等深度学习方法，并系统总结了公共数据集、评估指标、分类、检测等多种深度学习应用，探讨了细胞学计算的挑战和未来研究方向。