82 citations · 115 across the 8 of their papers we have counts for
22 papers
Continuous-Time Deep Glioma Growth Models
Jens Petersen, Fabian Isensee, Gregor Köhler +9
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation t…
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Tobias Roß, Pierangela Bruno, Annika Reinke +12
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step fo…
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Jens Petersen, Gregor Köhler, David Zimmerer +3
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test…
Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI
Peter M. Full, Fabian Isensee, Paul F. Jäger +1
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic seg…
nnU-Net for Brain Tumor Segmentation
Fabian Isensee, Paul F. Jaeger, Peter M. Full +2
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-s…
OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images
Fabian Isensee, Klaus H. Maier-Hein
Segmentation of endoscopic images is an essential processing step for computer and robotics-assisted interventions. The Robust-MIS challenge provides the largest dataset of annotat…