47 citations · 56 across the 4 of their papers we have counts for
6 papers · 1 filter
Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
Sergio Pereira, Raphael Meier, Victor Alves +2
Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into high…
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Adriano Pinto, Sergio Pereira, Raphael Meier +4
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life. To perform the revascularizat…
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
Andreas Hess, Raphael Meier, Johannes Kaesmacher +5
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The propo…
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…