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dc.contributor.authorDimitriou, Neofytos
dc.contributor.authorArandjelović, Ognjen
dc.contributor.authorCaie, Peter D.
dc.date.accessioned2019-11-29T10:30:04Z
dc.date.available2019-11-29T10:30:04Z
dc.date.issued2019-11-22
dc.identifier.citationDimitriou , N , Arandjelović , O & Caie , P D 2019 , ' Deep learning for whole slide image analysis : an overview ' , Frontiers in Medicine , vol. 6 , 264 . https://doi.org/10.3389/fmed.2019.00264en
dc.identifier.issn2296-858X
dc.identifier.otherPURE: 263809420
dc.identifier.otherPURE UUID: ee937277-fdad-4b01-8bd1-5f5b11bda8b7
dc.identifier.otherBibtex: 10.3389/fmed.2019.00264
dc.identifier.otherScopus: 85076684294
dc.identifier.otherWOS: 000501254900001
dc.identifier.urihttp://hdl.handle.net/10023/19028
dc.description.abstractThe widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
dc.format.extent7
dc.language.isoeng
dc.relation.ispartofFrontiers in Medicineen
dc.rightsCopyright © 2019 Dimitriou, Arandjelović and Caie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.subjectDigital pathologyen
dc.subjectComputer visionen
dc.subjectOncologyen
dc.subjectCanceren
dc.subjectMachine learningen
dc.subjectPersonalized pathologyen
dc.subjectImage analysisen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccRC0254en
dc.subject.lccQA75en
dc.titleDeep learning for whole slide image analysis : an overviewen
dc.typeJournal itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews.Centre for Biophotonicsen
dc.contributor.institutionUniversity of St Andrews.Cellular Medicine Divisionen
dc.identifier.doihttps://doi.org/10.3389/fmed.2019.00264
dc.description.statusPeer revieweden
dc.identifier.urlhttps://www.frontiersin.org/articles/10.3389/fmed.2020.00419/fullen


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