9.8k citations · 10.6k across the 12 of their papers we have counts for
4 papers · 1 filter
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
David Zimmerer, Simon A. A. Kohl, Jens Petersen +2
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detec…
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Paul F. Jaeger, Simon A. A. Kohl, Sebastian Bickelhaupt +4
The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the co…
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…