activity
20172021
most citedPerturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs

3 citations · 3 across the 1 of their papers we have counts for

collaborators

8 papers

cs.CV2021

The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference

Fabian Balsiger, Alain Jungo, Naren Akash R J +12

The MICCAI conference has encountered tremendous growth over the last years in terms of the size of the community, as well as the number of contributions and their technical succes…

eess.IV2020

Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks

Fabian Balsiger, Alain Jungo, Olivier Scheidegger +2

Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matchi…

eess.IV2019

Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting

Fabian Balsiger, Alain Jungo, Olivier Scheidegger +3

Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency, adop…

eess.IV2019

Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation

Alain Jungo, Mauricio Reyes

Despite the recent improvements in overall accuracy, deep learning systems still exhibit low levels of robustness. Detecting possible failures is critical for a successful clinical…

cs.CV2018

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Yannick Suter, Alain Jungo, Michael Rebsamen +4

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. I…

cs.CV2018

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

Alain Jungo, Raphael Meier, Ekin Ermis +2

Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlyi…