4 papers
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…
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…
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…
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
Jennifer Kamphenkel, Paul F. Jaeger, Sebastian Bickelhaupt +8
End-to-end deep learning improves breast cancer classification on diffusion-weighted MR images (DWI) using a convolutional neural network (CNN) architecture. A limitation of CNN as…