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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Format: | Book |
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Elsevier,
2022-01-01T00:00:00Z.
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A1234.567 |
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