Recommendations on test datasets for evaluating AI solutions in pathology

no code implementations • 21 Apr 2022 • André Homeyer, Christian Geißler, Lars Ole Schwen, Falk Zakrzewski, Theodore Evans, Klaus Strohmenger, Max Westphal, Roman David Bülow, Michaela Kargl, Aray Karjauv, Isidre Munné-Bertran, Carl Orge Retzlaff, Adrià Romero-López, Tomasz Sołtysiński, Markus Plass, Rita Carvalho, Peter Steinbach, Yu-Chia Lan, Nassim Bouteldja, David Haber, Mateo Rojas-Carulla, Alireza Vafaei Sadr, Matthias Kraft, Daniel Krüger, Rutger Fick, Tobias Lang, Peter Boor, Heimo Müller, Peter Hufnagl, Norman Zerbe

The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures.

Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning

no code implementations • 16 Dec 2021 • Nassim Bouteldja, Barbara Mara Klinkhammer, Tarek Schlaich, Peter Boor, Dorit Merhof

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.

Image-to-Image Translation Meta-Learning +3