Go to content
UR Home

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

URN to cite this document:
DOI to cite this document:
Berijanian, Maryam ; Schaadt, Nadine S. ; Huang, Boqiang ; Lotz, Johannes ; Feuerhake, Friedrich ; Merhof, Dorit
Date of publication of this fulltext: 13 Feb 2023 17:22


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 ...


Owner only: item control page
  1. Homepage UR

University Library

Publication Server


Publishing: oa@ur.de
0941 943 -4239 or -69394

Dissertations: dissertationen@ur.de
0941 943 -3904

Research data: datahub@ur.de
0941 943 -5707

Contact persons