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dc.contributor.authorNiazi, M Khalid Khan
dc.contributor.authorLin, Y
dc.contributor.authorLiu, F
dc.contributor.authorAshok, A
dc.contributor.authorMarcellin, M W
dc.contributor.authorTozbikian, G
dc.contributor.authorGurcan, M N
dc.contributor.authorBilgin, A
dc.date.accessioned2019-05-08T20:08:01Z
dc.date.available2019-05-08T20:08:01Z
dc.date.issued2019-04-01
dc.identifier.citationNiazi, M. K. K., Lin, Y., Liu, F., Ashok, A., Marcellin, M. W., Tozbikian, G., ... & Bilgin, A. (2019). Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer. Artificial intelligence in medicine, 95, 82-87.en_US
dc.identifier.issn1873-2860
dc.identifier.pmid30266546
dc.identifier.doi10.1016/j.artmed.2018.09.002
dc.identifier.urihttp://hdl.handle.net/10150/632217
dc.description.abstractIn this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine. The proposed framework was integrated into hotspot detection from images of breast biopsies, yielding considerable reduction of data and computing requirements.en_US
dc.description.sponsorshipNational Institutes of Health [NCI 1U01CA198945-01]en_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE BVen_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0933365717302324?via%3Dihuben_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.subjectAlpha shapesen_US
dc.subjectCompressionen_US
dc.subjectHotspot detectionen_US
dc.subjectJPIPen_US
dc.subjectKi-67en_US
dc.subjectPathology imagesen_US
dc.titlePathological image compression for big data image analysis: Application to hotspot detection in breast canceren_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizonaen_US
dc.identifier.journalARTIFICIAL INTELLIGENCE IN MEDICINEen_US
dc.description.note12 month embargo; published online: 25 September 2018en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleArtificial intelligence in medicine


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