Virtual staining for histology by deep learning

dc.contributor.authorLatonen, Leena
dc.contributor.authorKoivukoski, Sonja
dc.contributor.authorKhan, Umair
dc.contributor.authorRuusuvuori, Pekka
dc.contributor.organizationfi=biolääketieteen laitos|en=Institute of Biomedicine|
dc.contributor.organization-code1.2.246.10.2458963.20.77952289591
dc.converis.publication-id387296210
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/387296210
dc.date.accessioned2025-08-28T01:10:52Z
dc.date.available2025-08-28T01:10:52Z
dc.description.abstract<p>In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.</p>
dc.format.pagerange1177
dc.format.pagerange1191
dc.identifier.eissn1879-3096
dc.identifier.jour-issn0167-7799
dc.identifier.olddbid207148
dc.identifier.oldhandle10024/190175
dc.identifier.urihttps://www.utupub.fi/handle/11111/50629
dc.identifier.urlhttps://doi.org/10.1016/j.tibtech.2024.02.009
dc.identifier.urnURN:NBN:fi-fe2025082791526
dc.language.isoen
dc.okm.affiliatedauthorKhan, Umair
dc.okm.affiliatedauthorRuusuvuori, Pekka
dc.okm.discipline3111 Biomedicineen_GB
dc.okm.discipline3111 Biolääketieteetfi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA2 Scientific Article
dc.publisherCell Press
dc.publisher.countryNetherlandsen_GB
dc.publisher.countryAlankomaatfi_FI
dc.publisher.country-codeNL
dc.relation.doi10.1016/j.tibtech.2024.02.009
dc.relation.ispartofjournalTrends in Biotechnology
dc.relation.issue9
dc.relation.volume42
dc.source.identifierhttps://www.utupub.fi/handle/10024/190175
dc.titleVirtual staining for histology by deep learning
dc.year.issued2024

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