Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction. - Yannick Suter; Alain Jungo; Michael Rebsamen; Urspeter Knecht; Evelyn Herrmann; Roland Wiest; Mauricio Reyes 001 (BrainLes@MICCAI (2), 2018)

Triplanar Ensemble of 3D-to-2D CNNs with Label-Uncertainty for Brain Tumor Segmentation. - Richard McKinley; Michael Rebsamen; Raphael Meier; Roland Wiest (BrainLes@MICCAI (1), 2019)

Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019. - Michael Rebsamen; Christian Rummel; Ines Mürner-Lavanchy; Mauricio Reyes 001; Roland Wiest; Richard McKinley (ABCD-NP@MICCAI, 2019)

Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks. - Richard McKinley; Michael Rebsamen; Raphael Meier; Mauricio Reyes 001; Christian Rummel; Roland Wiest (CoRR, 2019)

Uncertainty-Driven Refinement of Tumor-Core Segmentation Using 3D-to-2D Networks with Label Uncertainty. - Richard McKinley; Michael Rebsamen; Katrin Daetwyler; Raphael Meier; Piotr Radojewski; Roland Wiest (BrainLes@MICCAI (1), 2020)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. - Raghav Mehta; Angelos Filos; Ujjwal Baid; Chiharu Sako; Richard McKinley; Michael Rebsamen; Katrin Dätwyler; Raphael Meier; Piotr Radojewski; Gowtham Krishnan Murugesan; Sahil S. Nalawade; Chandan Ganesh; et al. (CoRR, 2021)