A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. - Victor Mocioiu; Nuno Miguel Pedrosa de Barros; Sandra Ortega-Martorell; Johannes Slotboom; Urspeter Knecht; Carles Arús; Alfredo Vellido; Margarida Julià-Sapé (ESANN, 2016)

CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias. - Raphael Meier; Urspeter Knecht; Roland Wiest; Mauricio Reyes 001 (BrainLes@MICCAI, 2016)

Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction. - Alain Jungo; Richard McKinley; Raphael Meier; Urspeter Knecht; Luis Vera; Julián Pérez-Beteta; David Molina-García; Víctor M. Pérez-García; Roland Wiest; Mauricio Reyes 001 (BrainLes@MICCAI, 2017)

Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs. - Raphael Meier; Urspeter Knecht; Alain Jungo; Roland Wiest; Mauricio Reyes 001 (CoRR, 2017)

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)

Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors. - Yannick Suter; Urspeter Knecht; Roland Wiest; Mauricio Reyes 001 (BrainLes@MICCAI (1), 2020)

Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning. - Yannick Suter; Urspeter Knecht; Roland Wiest; Ekkehard Hewer; Philippe Schucht; Mauricio Reyes 001 (MLCN/RNO-AI@MICCAI, 2020)

Towards Radiomics-Based Automated Disease Progression Assessment for Glioblastoma Patients. - Yannick Suter; Flurina Schuhmacher; Ekin Ermis; Urspeter Knecht; Philippe Schucht; Roland Wiest; Mauricio Reyes 001 (BrainLes/SWITCH@MICCAI, 2023)