Normalization of HE-stained histological images using cycle consistent generative adversarial networks

BACKGROUND: Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these vari...

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Detalles Bibliográficos
Autores principales: Runz, Marlen, Rusche, Daniel, Schmidt, Stefan, Weihrauch, Martin R., Hesser, Jürgen, Weis, Cleo-Aron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349020/
https://www.ncbi.nlm.nih.gov/pubmed/34362386
http://dx.doi.org/10.1186/s13000-021-01126-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349020/
https://www.ncbi.nlm.nih.gov/pubmed/34362386
http://dx.doi.org/10.1186/s13000-021-01126-y

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