| Analyzing the Quality and Challenges of Uncertainty Estimations for Brain Tumor Segmentation A Jungo, F Balsiger, M Reyes Frontiers in Neuroscience 14, 282, 2020 | 71 | 2020 |
| Magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks F Balsiger, A Shridhar Konar, S Chikop, V Chandran, O Scheidegger, ... Machine Learning for Medical Image Reconstruction: First International …, 2018 | 52 | 2018 |
| pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis A Jungo, O Scheidegger, M Reyes, F Balsiger Computer methods and programs in biomedicine 198, 105796, 2021 | 43 | 2021 |
| Segmentation of peripheral nerves from magnetic resonance neurography: a fully-automatic, deep learning-based approach F Balsiger, C Steindel, M Arn, B Wagner, L Grunder, M El-Koussy, ... Frontiers in neurology, 777, 2018 | 38 | 2018 |
| Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient F Kofler, I Ezhov, F Isensee, F Balsiger, C Berger, M Koerner, J Paetzold, ... arXiv preprint arXiv:2103.06205, 2021 | 37 | 2021 |
| Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting F Balsiger, A Jungo, O Scheidegger, PG Carlier, M Reyes, B Marty Medical image analysis 64, 101741, 2020 | 23 | 2020 |
| Learning shape representation on sparse point clouds for volumetric image segmentation F Balsiger, Y Soom, O Scheidegger, M Reyes Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 18 | 2019 |
| Are we using appropriate segmentation metrics F Kofler, I Ezhov, F Isensee, F Balsiger, C Berger, M Koerner, J Paetzold, ... Identifying correlates of human expert perception for CNN training beyond …, 2021 | 11 | 2021 |
| On the Spatial and Temporal Influence for the Reconstruction of Magnetic Resonance Fingerprinting F Balsiger, O Scheidegger, PG Carlier, B Marty, M Reyes International Conference on Medical Imaging with Deep Learning, 27-38, 2019 | 9 | 2019 |
| Quantitative water T2 relaxometry in the early detection of neuromuscular diseases: a retrospective biopsy-controlled analysis N Locher, B Wagner, F Balsiger, O Scheidegger European radiology 32 (11), 7910-7917, 2022 | 4 | 2022 |
| Medical-Blocks―A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research: System Development and Integration Results W Valenzuela, F Balsiger, R Wiest, O Scheidegger JMIR formative research 6 (4), e32287, 2022 | 4 | 2022 |
| Learning bloch simulations for MR fingerprinting by invertible neural networks F Balsiger, A Jungo, O Scheidegger, B Marty, M Reyes Machine Learning for Medical Image Reconstruction: Third International …, 2020 | 4 | 2020 |
| Quantification of fat fraction and water T1 in neuromuscular diseases using deep learning-based magnetic resonance fingerprinting with water and fat separation F Balsiger, M Reyes, O Scheidegger, PG Carlier, B Marty Imaging Neuromusc Dis 25, 2019 | 2 | 2019 |
| Methodologies and MR Parameters in Quantitative Magnetic Resonance Neurography: A Scoping Review Protocol F Balsiger, B Wagner, JME Jende, B Marty, M Bendszus, O Scheidegger, ... Methods and Protocols 5 (3), 39, 2022 | | 2022 |
| The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference F Balsiger, A Jungo, J Chen, I Ezhov, S Liu, J Ma, JC Paetzold, ... arXiv preprint arXiv:2103.05437, 2021 | | 2021 |
| P13. Semi-automatic, machine-learning based segmentation of peripheral nerves for quantitative morphometry: Comparison of low-and high-resolution MR neurography F Balsiger, C Steindel, M Arn, B Wagner, M El-Koussy, KM Rösler, ... Clinical Neurophysiology 129 (8), e70-e71, 2018 | | 2018 |
| Medical Image Analysis Laboratory (MIALab): An Educational Approach to Medical Image Analysis using Machine Learning F Balsiger, A Jungo, Y Suter, M Reyes | | |
Mauricio ReyesMedical Image Analysis, ARTORG Center, Univ. BernVerified email at med.unibe.ch
