Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2653
Title: BreCaHAD: A dataset for breast cancer histopathological annotation and diagnosis
Authors: Aksaç, Alper
Demetrick, D. J.
Özyer, Tansel
Alhajj, Reda
143116
Keywords: Medical imaging 
 pathology 
 histopathological images
Issue Date: 2019
Publisher: BioMed Central Ltd.
Source: Aksac, A., Demetrick, D. J., Ozyer, T., and Alhajj, R. (2019). BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC research notes, 12(1), 82.
Abstract: Objectives: Histopathological tissue analysis by a pathologist determines the diagnosis and prognosis of most tumors, such as breast cancer. To estimate the aggressiveness of cancer, a pathologist evaluates the microscopic appearance of a biopsied tissue sample based on morphological features which have been correlated with patient outcome. Data description: This paper introduces a dataset of 162 breast cancer histopathology images, namely the breast cancer histopathological annotation and diagnosis dataset (BreCaHAD) which allows researchers to optimize and evaluate the usefulness of their proposed methods. The dataset includes various malignant cases. The task associated with this dataset is to automatically classify histological structures in these hematoxylin and eosin (H&E) stained images into six classes, namely mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule. By providing this dataset to the biomedical imaging community, we hope to encourage researchers in computer vision, machine learning and medical fields to contribute and develop methods/tools for automatic detection and diagnosis of cancerous regions in breast cancer histology images. © 2019 The Author(s).
URI: https://tinyurl.com/tsarue4
https://hdl.handle.net/20.500.11851/2653
ISSN: 17560500
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

Files in This Item:
File Description SizeFormat 
Ozyer_BreCaHAD.pdf501.69 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

15
checked on Jul 13, 2022

WEB OF SCIENCETM
Citations

18
checked on Jul 14, 2022

Page view(s)

90
checked on Aug 8, 2022

Download(s)

2
checked on Aug 8, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.