Improving unsupervised stain-to-stain translation using self-supervision and meta-learning

BACKGROUND: In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on image-to-image translation is gaining importance in thi...

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Detalles Bibliográficos
Autores principales: Bouteldja, Nassim, Klinkhammer, Barbara M., Schlaich, Tarek, Boor, Peter, Merhof, Dorit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577059/
https://www.ncbi.nlm.nih.gov/pubmed/36268068
http://dx.doi.org/10.1016/j.jpi.2022.100107

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577059/
https://www.ncbi.nlm.nih.gov/pubmed/36268068
http://dx.doi.org/10.1016/j.jpi.2022.100107

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