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CCAgT.yml
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CCAgT.yml
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---
title: 'Cytology Dataset – CCAgT: Images of Cervical Cells with AgNOR Stain Technique'
homepage: https://doi.org/10.17632/wg4bpm33hj.2
category: ImageProcessing
description: 'Contains 9339 images with resolution of 1600×1200 where each pixel is 0.111µmX0.111µm from 15 different slides stained with AgNOR technique, having at least one label per image. Have more than sixty-three thousand annotations. The images from patients of Gynecology and Colonoscopy Outpatient Clinic of the Polydoro Ernani de São Thiago University Hospital of the Universidade Federal de Santa Catarina (HU-UFSC). This research was approved by the UFSC Research Ethics Committee (CEPSH), protocol number 57423616.3.0000.0121. First, all patients involved were informed about the objectives of the study, and those who agreed to participate signed an informed consent form.'
version: 2.0
keywords: Cytology, AgNOR, image segmentation, object detection, semantic segmentation.
image: https://arquivos.ufsc.br/d/373be2177a33426a9e6c/files/?p=/images/train/2019_11_14__09_25__0165_s0c0x100661-1600y144473-1200m21402.png
temporal: 2018-2019
spatial: 1600*1200
access_level: public
copyrights: UFSC, LAPiX and the Authors Joao Gustavo A. Amorim, Andre Victoria Matias, Tainee Bottamedi, Vinícius Sanches, Ane Francyne Costa, Fabiana Botelho De Miranda Onofre, Alexandre Sherlley C. Onofre, Aldo Von Wangenheim
accrual_periodicity:
specification: PNG, JSON
data_quality: true
data_dictionary: At readme from https://doi.org/10.17632/wg4bpm33hj.2
language: en
license: CC BY NC 3.0
publisher:
- name:
web:
organization:
- name: Image Processing and Computer Graphics Lab (LAPiX), INCoD, Department of Informatics and Statistics, Federal University of Santa Catarina - UFSC, Brazil
web: www.lapix.ufsc.br/
issued_time: 2020.08
sources:
- name: 'Cytology Dataset – CCAgT: Images of Cervical Cells with AgNOR Stain Technique'
access_url: https://www.lapix.ufsc.br/agnor-dataset/
- name: 'Images of Cervical Cells with AgNOR Stain Technique'
access_url: https://huggingface.co/datasets/lapix/CCAgT
references:
- title: A Novel Approach on Segmentation of AgNOR-Stained Cytology Images Using Deep Learning
reference: https://doi.org/10.1109/CBMS49503.2020.00110
- title: Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the AgNOR Technique
reference: https://dx.doi.org/10.2139/ssrn.4126881