116 citations · 297 across the 6 of their papers we have counts for
6 papers · 1 filter
Reg R-CNN: Lesion Detection and Grading under Noisy Labels
Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl +1
For the task of concurrently detecting and categorizing objects, the medical imaging community commonly adopts methods developed on natural images. Current state-of-the-art object…
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein +5
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest,…
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl +2
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithm…
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Fabian Isensee, Jens Petersen, Andre Klein +8
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation…
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer +6
Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer…
Adversarial Networks for the Detection of Aggressive Prostate Cancer
Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer +5
Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of train…