Mixture of learners for cancer stem cell detection using CD13 and H and E stained images

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage16en_US
dc.citation.spage1en_US
dc.citation.volumeNumber9791en_US
dc.contributor.authorOğuz, Oğuzhanen_US
dc.contributor.authorAkbaş, Cem Emreen_US
dc.contributor.authorMallah, Maenen_US
dc.contributor.authorTaşdemir, K.en_US
dc.contributor.authorAkhan-Güzelcan, E.en_US
dc.contributor.authorMuenzenmayer, C.en_US
dc.contributor.authorWittenberg, T.en_US
dc.contributor.authorÜner, A.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.authorÇetin-Atalay, R.en_US
dc.coverage.spatialSan Diego, California, United Statesen_US
dc.date.accessioned2018-04-12T11:47:14Z
dc.date.available2018-04-12T11:47:14Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 27 February-3 March 2016en_US
dc.descriptionConference Name: Medical Imaging 2016: Digital Pathology, SPIE 2016en_US
dc.description.abstractIn this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-Analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:47:14Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1117/12.2216113en_US
dc.identifier.issn1605-7422en_US
dc.identifier.urihttp://hdl.handle.net/11693/37663
dc.language.isoEnglishen_US
dc.publisherSPIEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1117/12.2216113en_US
dc.source.titleProceedings of SPIE Vol. 9791, Medical Imaging 2016: Digital Pathology, SPIE 2016en_US
dc.subject1-D SIFTen_US
dc.subjectCancer stem cell detectionen_US
dc.subjectCD13 stainen_US
dc.subjectEigenfaceen_US
dc.subjectH&E stainen_US
dc.subjectOnline learningen_US
dc.subjectRegion codifierence descriptoren_US
dc.subjectRegion covariance descriptoren_US
dc.titleMixture of learners for cancer stem cell detection using CD13 and H and E stained imagesen_US
dc.typeConference Paperen_US

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