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Unsupervised many-to-many stain translation for histological image augmentation to improve classification accuracy

URN to cite this document:
urn:nbn:de:bvb:355-epub-537371
DOI to cite this document:
10.5283/epub.53737
Berijanian, Maryam ; Schaadt, Nadine S. ; Huang, Boqiang ; Lotz, Johannes ; Feuerhake, Friedrich ; Merhof, Dorit
Date of publication of this fulltext: 13 Feb 2023 17:22



Abstract

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 variability of images. Coping with this problem requires methods such as image augmentation and ...

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