Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks

Richard McKinley, Michael Rebsamen, Raphael Meier +3

Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment

Sergio Pereira, Raphael Meier, Victor Alves +2

Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning

Andreas Hess, Raphael Meier, Johannes Kaesmacher +5

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, Mauricio Reyes, Andras Jakab +421

Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation

Alain Jungo, Raphael Meier, Ekin Ermis +2

On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation

Alain Jungo, Raphael Meier, Ekin Ermis +4

Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation

Raphael Meier, Michael Rebsamen, Urspeter Knecht +3

The paper proposes two training methods that incorporate tumor grade information to improve deep learning segmentation of brain tumors.

Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction

Adriano Pinto, Sergio Pereira, Raphael Meier +4

Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs

Raphael Meier, Urspeter Knecht, Alain Jungo +2