  Analyzing Pap cytology slides is an important tasks in detecting and grading
precancerous and cancerous cervical cancer stages. Processing cytology images
usually involve segmenting nuclei and overlapping cells. We introduce a
cervical cytology dataset that can be used to evaluate nucleus detection, as
well as image classification methods in the cytology image processing area.
This dataset contains 93 real image stacks with their grade labels and manually
annotated nuclei within images. We also present two methods: a baseline method
based on a previously proposed approach, and a deep learning method, and
compare their results with other state-of-the-art methods. Both the baseline
method and the deep learning method outperform other state-of-the-art methods
by significant margins. Along with the dataset, we publicly make the evaluation
code and the baseline method available to download for further benchmarking.
