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...
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| Main Authors: | , , , , , |
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| Format: | Book |
| Published: |
Elsevier,
2023-01-01T00:00:00Z.
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| Subjects: | |
| Online Access: | Connect to this object online. |
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Internet
Connect to this object online.3rd Floor Main Library
| Call Number: |
A1234.567 |
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| Copy 1 | Available |