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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...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2023
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| Materias: | |
| 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 |