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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |