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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2022
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| Materias: | |
| 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 |