1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Άρθρα/Articles
Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1925
DC FieldValueLanguage
dc.contributor.authorSchnorrenberg, Frank-
dc.contributor.authorTsapatsoulis, Nicolas-
dc.contributor.authorPattichis, Constantinos S.-
dc.contributor.authorSchizas, Christos N.-
dc.contributor.authorKollias, Stefanos D.-
dc.contributor.authorVassiliou, Mary-
dc.contributor.authorAdamou, Adamos-
dc.contributor.authorKyriacou, Kyriacos C.-
dc.date.accessioned2009-05-26T12:52:28Zen
dc.date.accessioned2013-05-16T13:11:01Z-
dc.date.accessioned2015-12-02T09:40:14Z-
dc.date.available2009-05-26T12:52:28Zen
dc.date.available2013-05-16T13:11:01Z-
dc.date.available2015-12-02T09:40:14Z-
dc.date.issued2000-01-
dc.identifier.citationIEEE Engineering in Medicine and Biology Magazine, 2000, vol. 19, no. 1, pp.48-63en_US
dc.identifier.issn07395175-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1925-
dc.description.abstractDiscusses the analysis of nuclei in histopathological sections with a system that closely simulates human experts. The evaluation of immunocytochemically stained histopathological sections presents a complex problem due to many variations that are inherent in the methodology. In this respect, many aspects of immunocytochemistry remain unresolved, despite the fact that results may carry important diagnostic, prognostic, and therapeutic information. In this article, a modular neural network-based approach to the detection and classification of breast cancer nuclei stained for steroid receptors in histopathological sections is described and evaluated.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Engineering in Medicine and Biology Magazineen_US
dc.rights© IEEE Engineering in Medicine and Biology Societyen_US
dc.subjectBiomedicalen_US
dc.subjectBreast cancer Nucleien_US
dc.subjectModular Neural Networken_US
dc.titleImproved Detection of Breast Cancer Nuclei using Modular Neural Networksen_US
dc.typeArticleen_US
dc.collaborationUniversity of Cyprusen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1109/51.816244en_US
dc.dept.handle123456789/54en
dc.relation.issue1en_US
dc.relation.volume19en_US
cut.common.academicyear2000-2001en_US
dc.identifier.spage48en_US
dc.identifier.epage63en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.languageiso639-1en-
crisitem.author.deptDepartment of Communication and Marketing-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0002-6739-8602-
crisitem.author.parentorgFaculty of Communication and Media Studies-
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