114 citations · 189 across the 7 of their papers we have counts for
6 papers · 1 filter
Deep Learning-based Type Identification of Volumetric MRI Sequences
Jean Pablo Vieira de Mello, Thiago M. Paixão, Rodrigo Berriel +4
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor. As the interest for automatizing brain volume…
Combining unsupervised and supervised learning for predicting the final stroke lesion
Adriano Pinto, Sérgio Pereira, Raphael Meier +4
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-…
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…
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…
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation
Raphael Meier, Michael Rebsamen, Urspeter Knecht +3
Deep learning methods for brain tumor segmentation are typically trained in an ad hoc fashion on all available data. Brain tumors are tremendously heterogeneous in image appearance…
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…