44 citations · 120 across the 10 of their papers we have counts for
6 papers · 1 filter
Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery
Alain Jungo, Lars Doorenbos, Tommaso Da Col +4
Purpose: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Det…
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…
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…
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…
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Alain Jungo, Raphael Meier, Ekin Ermis +4
Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e.g., neurosurgical interventions, r…
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Raphael Meier, Urspeter Knecht, Alain Jungo +2
This paper proposes a novel approach for uncertainty quantification in dense Conditional Random Fields (CRFs). The presented approach, called Perturb-and-MPM, enables efficient, ap…