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Generative deep learning in digital pathology workflows
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dc.contributor.author | Morrison, David | |
dc.contributor.author | Harris-Birtill, David | |
dc.contributor.author | Caie, Peter D. | |
dc.date.accessioned | 2022-05-09T14:30:22Z | |
dc.date.available | 2022-05-09T14:30:22Z | |
dc.date.issued | 2021-10-01 | |
dc.identifier | 273720067 | |
dc.identifier | 42b31ebe-7236-4ed4-9d58-458c172db842 | |
dc.identifier | 85108833516 | |
dc.identifier | 000701844500008 | |
dc.identifier.citation | Morrison , 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.2021.02.024 | en |
dc.identifier.issn | 0002-9440 | |
dc.identifier.other | RIS: urn:DD56D25A11B534E99F851B2D0C8F53BA | |
dc.identifier.uri | https://hdl.handle.net/10023/25322 | |
dc.description | Funding: 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.abstract | Many 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.extent | 6 | |
dc.format.extent | 688328 | |
dc.language.iso | eng | |
dc.relation.ispartof | The American Journal of Pathology | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | QA76 Computer software | en |
dc.subject | RB Pathology | en |
dc.subject | AC | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | QA76 | en |
dc.subject.lcc | RB | en |
dc.title | Generative deep learning in digital pathology workflows | en |
dc.type | Journal item | en |
dc.contributor.sponsor | Technology Strategy Board | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.contributor.institution | University of St Andrews. Centre for Biophotonics | en |
dc.contributor.institution | University of St Andrews. Cellular Medicine Division | en |
dc.identifier.doi | https://doi.org/10.1016/j.ajpath.2021.02.024 | |
dc.description.status | Peer reviewed | en |
dc.date.embargoedUntil | 2022-04-08 | |
dc.identifier.url | https://www.sciencedirect.com/journal/the-american-journal-of-pathology/vol/191/issue/10 | en |
dc.identifier.grantnumber | TS/S013121/1 | en |
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