78 citations · 164 across the 3 of their papers we have counts for
4 papers
Recommendations on test datasets for evaluating AI solutions in pathology
André Homeyer, Christian Geißler, Lars Ole Schwen +27
Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to…
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning
Nassim Bouteldja, Barbara Mara Klinkhammer, Tarek Schlaich +2
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 i…
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
Michael Gadermayr, Laxmi Gupta, Barbara M. Klinkhammer +2
Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios. In this paper, we go even further by proposing a fully unsup…
CNN Cascades for Segmenting Whole Slide Images of the Kidney
Michael Gadermayr, Ann-Kathrin Dombrowski, Barbara Mara Klinkhammer +2
Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based ima…