162 citations · 625 across the 28 of their papers we have counts for
4 papers · 1 filter
Fooling the Crowd with Deep Learning-based Methods
Christian Marzahl, Marc Aubreville, Christof A. Bertram +6
Modern, state-of-the-art deep learning approaches yield human like performance in numerous object detection and classification tasks. The foundation for their success is the availa…
Learning New Tricks from Old Dogs -- Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment
Marc Aubreville, Christof A. Bertram, Samir Jabari +3
For histopathological tumor assessment, the count of mitotic figures per area is an important part of prognostication. Algorithmic approaches - such as for mitotic figure identific…
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
Christian Marzahl, Marc Aubreville, Christof A. Bertram +13
Purpose: Exercise-induced pulmonary hemorrhage (EIPH) is a common syndrome in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a…
Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
Marc Aubreville, Christof A. Bertram, Christian Marzahl +9
Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, st…