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dc.contributor.authorMorrison, David
dc.contributor.authorHarris-Birtill, David
dc.contributor.authorCaie, Peter D.
dc.date.accessioned2022-05-09T14:30:22Z
dc.date.available2022-05-09T14:30:22Z
dc.date.issued2021-10-01
dc.identifier.citationMorrison , D , Harris-Birtill , D & Caie , P D 2021 , ' Generative deep learning in digital pathology workflows ' , The American Journal of Pathology , vol. 191 , no. 10 , pp. 1717-1723 . https://doi.org/10.1016/j.ajpath.2021.02.024en
dc.identifier.issn0002-9440
dc.identifier.otherPURE: 273720067
dc.identifier.otherPURE UUID: 42b31ebe-7236-4ed4-9d58-458c172db842
dc.identifier.otherRIS: urn:DD56D25A11B534E99F851B2D0C8F53BA
dc.identifier.otherScopus: 85108833516
dc.identifier.otherWOS: 000701844500008
dc.identifier.urihttp://hdl.handle.net/10023/25322
dc.descriptionFunding: Supported by the Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews and Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (grant number TS/S013121/1).en
dc.description.abstractMany modern histopathology laboratories are in the process of digitising their workflows. Once images of the tissue exist as digital data, it becomes feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on Deep Learning, promise systems that can identify pathologies in slide images with a high degree of accuracy. Generative modelling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology including the removal of color and intensity artefacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses some future directions for generative models within histopathology.
dc.format.extent6
dc.language.isoeng
dc.relation.ispartofThe American Journal of Pathologyen
dc.rightsCopyright © 2021 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.ajpath.2021.02.024.en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA76 Computer softwareen
dc.subjectRB Pathologyen
dc.subjectACen
dc.subject.lccQA75en
dc.subject.lccQA76en
dc.subject.lccRBen
dc.titleGenerative deep learning in digital pathology workflowsen
dc.typeJournal itemen
dc.contributor.sponsorTechnology Strategy Boarden
dc.description.versionPostprinten
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/10.1016/j.ajpath.2021.02.024
dc.description.statusPeer revieweden
dc.date.embargoedUntil2022-04-08
dc.identifier.urlhttps://www.sciencedirect.com/journal/the-american-journal-of-pathology/vol/191/issue/10en
dc.identifier.grantnumberTS/S013121/1en


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