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dc.contributor.authorSchnorrenberg, F.en
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorKyriacou, Kyriacos C.en
dc.contributor.authorSchizas, Christos N.en
dc.creatorSchnorrenberg, F.en
dc.creatorPattichis, Constantinos S.en
dc.creatorKyriacou, Kyriacos C.en
dc.creatorSchizas, Christos N.en
dc.date.accessioned2019-11-13T10:42:14Z
dc.date.available2019-11-13T10:42:14Z
dc.date.issued1997
dc.identifier.issn1089-7771
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54959
dc.description.abstractA computer-aided detection system for tissue cell nuclei in histological sections is introduced and validated as part of the Biopsy Analysis Support System (BASS). Cell nuclei are selectively stained with monoclonal antibodies such as the antiestrogen receptor antibodies which are widely applied as part of assessing patient prognosis in breast cancer. The detection system uses a receptive field filter to enhance negatively and positively stained cell nuclei and a squashing function to label each pixel value as belonging to the background or a nucleus. In this study the detection system assessed all biopsies in an automated fashion. Detection and classification of individual nuclei as well as biopsy grading performance was shown to be promising as compared to that of two experts. Sensitivity and positive predictive value were measured to be 83% and 67.4% respectively. One major advantage of BASS stems from the fact that the system simulates the assessment procedures routinely employed by human expertsen
dc.description.abstractthus it can be used as an additional independent expert. Moreover the system allows the efficient accumulation of data from large numbers of nuclei in a short time span. Therefore the potential for accurate quantitative assessments is increased and a platform for more standardized evaluations is provided. © 1997 IEEE.en
dc.sourceIEEE Transactions on Information Technology in Biomedicineen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0031150036&partnerID=40&md5=af92204ed59b5ce06fe64af9b5a50f8b
dc.subjectSensitivity analysisen
dc.subjectarticleen
dc.subjectAlgorithmsen
dc.subjecthumanen
dc.subjectHumansen
dc.subjectBreast Neoplasmsen
dc.subjectfemaleen
dc.subjectalgorithmen
dc.subjectestrogen receptoren
dc.subjectprogesterone receptoren
dc.subjectpathologyen
dc.subjectmetabolismen
dc.subjectOncologyen
dc.subjectbreast tumoren
dc.subjectvalidation studyen
dc.subjectData acquisitionen
dc.subjectReceptors, Estrogenen
dc.subjectReceptors, Progesteroneen
dc.subjectcell nucleusen
dc.subjectComputer aided diagnosisen
dc.subjectcomputer assisted diagnosisen
dc.subjectDiagnosis, Computer-Assisteden
dc.subjectBiopsy analysis support system (BASS)en
dc.titleComputer-aided detection of breast cancer nucleien
dc.typeinfo:eu-repo/semantics/article
dc.description.volume1
dc.description.issue2
dc.description.startingpage128
dc.description.endingpage140
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :41</p>en
dc.source.abbreviationIEEE Trans.Inf.Technol.Biomed.en
dc.contributor.orcidSchizas, Christos N. [0000-0001-6548-4980]
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.gnosis.orcid0000-0001-6548-4980
dc.gnosis.orcid0000-0003-1271-8151


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