Unsupervised many-to-many stain translation for histological image augmentation to improve classification accuracy

BACKGROUND: Deep learning tasks, which require large numbers of images, are widely applied in digital pathology. This poses challenges especially for supervised tasks since manual image annotation is an expensive and laborious process. This situation deteriorates even more in the case of a large var...

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Detalles Bibliográficos
Autores principales: Berijanian, Maryam, Schaadt, Nadine S., Huang, Boqiang, Lotz, Johannes, Feuerhake, Friedrich, Merhof, Dorit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947329/
https://www.ncbi.nlm.nih.gov/pubmed/36844704
http://dx.doi.org/10.1016/j.jpi.2023.100195

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947329/
https://www.ncbi.nlm.nih.gov/pubmed/36844704
http://dx.doi.org/10.1016/j.jpi.2023.100195

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