Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2653
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dc.contributor.authorAksaç, Alper-
dc.contributor.authorDemetrick, D. J.-
dc.contributor.authorÖzyer, Tansel-
dc.contributor.authorAlhajj, Reda-
dc.date.accessioned2019-12-25T14:01:59Z
dc.date.available2019-12-25T14:01:59Z
dc.date.issued2019
dc.identifier.citationAksac, 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.en_US
dc.identifier.issn17560500
dc.identifier.urihttps://tinyurl.com/tsarue4-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2653-
dc.description.abstractObjectives: 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).en_US
dc.language.isoenen_US
dc.publisherBioMed Central Ltd.en_US
dc.relation.ispartofBMC Research Notesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMedical imaging en_US
dc.subject pathology en_US
dc.subject histopathological imagesen_US
dc.titleBrecahad: a Dataset for Breast Cancer Histopathological Annotation and Diagnosisen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume12
dc.identifier.issue1
dc.identifier.wosWOS:000458399800004en_US
dc.identifier.scopus2-s2.0-85061505856en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.pmid30755250en_US
dc.identifier.doi10.1186/s13104-019-4121-7-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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