  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.
