English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection