K. Strohmenger

Recommendations on test datasets for evaluating AI solutions in pathology

P. Boor, P. Hufnagl, Mateo Rojas-Carulla, 2022, ArXiv.

Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology

P. Boor, P. Hufnagl, Mateo Rojas-Carulla, 2022, Modern Pathology.

The vendor-agnostic EMPAIA platform for integrating AI applications into digital pathology infrastructures

B. Lindequist, L. O. Schwen, Christoph Jansen, 2022, Future Gener. Comput. Syst..

EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology

P. Hufnagl, Christoph Jansen, A. Homeyer, 2021, Comput. Methods Programs Biomed..

Curious Containers: A framework for computational reproducibility in life sciences with support for Deep Learning applications

Michael Witt, Peter Hufnagl, Felix Bartusch, 2020, Future Gener. Comput. Syst..

Reproducibility and Performance of Deep Learning Applications for Cancer Detection in Pathological Images

Christoph Jansen, Dagmar Krefting, Bruno Schilling, 2019, 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).