A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection
This work addresses the need for better nucleus detection and image classification in cervical cytology processing, which is crucial for early cancer diagnosis, but it is incremental as it builds on existing methods with a new dataset.
The authors tackled the problem of analyzing Pap cytology slides for cervical cancer detection by introducing a new dataset (Cervix93) with 93 real image stacks and manually annotated nuclei, and presented two methods that outperform state-of-the-art approaches by significant margins.
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.