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dc.contributor.authorOzdemir, E.en_US
dc.contributor.authorSokmensuer, C.en_US
dc.contributor.authorGunduz Demir, C.en_US
dc.date.accessioned2019-01-23T16:19:47Z
dc.date.available2019-01-23T16:19:47Z
dc.date.issued2012-01en_US
dc.identifier.issn0018-9294
dc.identifier.urihttp://hdl.handle.net/11693/48292
dc.description.abstractIn recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image variations. In order to meet this challenge, it is important to learn the systems on a large number of labeled images from a different range of variation. However, acquiring labeled images is quite difficult in this domain, and hence, the labeled training data are typically very limited. Although the issue of having limited labeled data is acknowledged by many researchers, it has rarely been considered in the system design. This paper successfully addresses this issue, introducing a new resampling framework to simulate variations in tissue images. This framework generates multiple sequences from an image for its representation and models them using a Markov process. Working with colon tissue images, our experiments show that this framework increases the generalization capacity of a learner by increasing the size and variation of the training data and improves the classification performance of a given image by combining the decisions obtained on its sequences.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Biomedical Engineeringen_US
dc.relation.isversionofhttps://ieeexplore.ieee.org/document/6062388en_US
dc.subjectAutomated cancer diagnosisen_US
dc.subjectCanceren_US
dc.subjectHistopathological image analysisen_US
dc.subjectMarkov modelsen_US
dc.subjectResamplingen_US
dc.titleA resampling-based Markovian model for automated colon cancer diagnosisen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage281en_US
dc.citation.epage289en_US
dc.citation.volumeNumber59en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1109/TBME.2011.2173934en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.identifier.eissn1558-2531


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