A gentle introduction to deep learning in medical image processing A Maier, C Syben, T Lasser, C Riess Zeitschrift für Medizinische Physik 29 (2), 86-101, 2019 | 584 | 2019 |
Learning with known operators reduces maximum error bounds AK Maier, C Syben, B Stimpel, T Würfl, M Hoffmann, F Schebesch, W Fu, ... Nature machine intelligence 1 (8), 373-380, 2019 | 180 | 2019 |
PYRO‐NN: Python reconstruction operators in neural networks C Syben, M Michen, B Stimpel, S Seitz, S Ploner, AK Maier Medical physics 46 (11), 5110-5115, 2019 | 70 | 2019 |
Precision learning: towards use of known operators in neural networks A Maier, F Schebesch, C Syben, T Würfl, S Steidl, JH Choi, R Fahrig 2018 24th International Conference on Pattern Recognition (ICPR), 183-188, 2018 | 64 | 2018 |
Multi-modal deep guided filtering for comprehensible medical image processing B Stimpel, C Syben, F Schirrmacher, P Hoelter, A Dörfler, A Maier IEEE transactions on medical imaging 39 (5), 1703-1711, 2019 | 37 | 2019 |
Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data K Packhäuser, S Gündel, N Münster, C Syben, V Christlein, A Maier Scientific Reports 12 (1), 14851, 2022 | 34 | 2022 |
Detecting anatomical landmarks for motion estimation in weight-bearing imaging of knees B Bier, K Aschoff, C Syben, M Unberath, M Levenston, G Gold, R Fahrig, ... Machine Learning for Medical Image Reconstruction: First International …, 2018 | 32 | 2018 |
Precision learning: reconstruction filter kernel discretization C Syben, B Stimpel, K Breininger, T Würfl, R Fahrig, A Dörfler, A Maier arXiv preprint arXiv:1710.06287, 2017 | 28 | 2017 |
Is medical chest X-ray data anonymous? K Packhäuser, S Gündel, N Münster, C Syben, V Christlein, A Maier | 25 | 2021 |
RinQ fingerprinting: recurrence-informed quantile networks for magnetic resonance fingerprinting E Hoppe, F Thamm, G Körzdörfer, C Syben, F Schirrmacher, M Nittka, ... International Conference on Medical Image Computing and Computer-Assisted …, 2019 | 24 | 2019 |
Deriving neural network architectures using precision learning: Parallel-to-fan beam conversion C Syben, B Stimpel, J Lommen, T Würfl, A Dörfler, A Maier Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany …, 2019 | 24 | 2019 |
Age estimation on panoramic dental X-ray images using deep learning S Wallraff, S Vesal, C Syben, R Lutz, A Maier Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on …, 2021 | 23 | 2021 |
MR to X-ray projection image synthesis B Stimpel, C Syben, T Würfl, K Mentl, A Dörfler, A Maier arXiv preprint arXiv:1710.07498, 2017 | 18 | 2017 |
Cephalogram synthesis and landmark detection in dental cone-beam CT systems Y Huang, F Fan, C Syben, P Roser, L Mill, A Maier Medical Image Analysis 70, 102028, 2021 | 17 | 2021 |
Maximum likelihood estimation of head motion using epipolar consistency A Preuhs, N Ravikumar, M Manhart, B Stimpel, E Hoppe, C Syben, ... Bildverarbeitung für die Medizin 2019: Algorithmen–Systeme–Anwendungen …, 2019 | 17 | 2019 |
Machine Learning for Medical Image Reconstruction B Bier, K Aschoff, C Syben, M Unberath, M Levenston, G Gold, R Fahrig, ... Springer, 2018 | 14 | 2018 |
X-ray scatter estimation using deep splines P Roser, A Birkhold, A Preuhs, C Syben, L Felsner, E Hoppe, N Strobel, ... IEEE Transactions on Medical Imaging 40 (9), 2272-2283, 2021 | 13 | 2021 |
Known operator learning enables constrained projection geometry conversion: Parallel to cone-beam for hybrid MR/X-ray imaging C Syben, B Stimpel, P Roser, A Dörfler, A Maier IEEE Transactions on Medical Imaging 39 (11), 3488-3498, 2020 | 13 | 2020 |
Multi-modal super-resolution with deep guided filtering B Stimpel, C Syben, F Schirrmacher, P Hoelter, A Dörfler, A Maier Bildverarbeitung für die Medizin 2019: Algorithmen–Systeme–Anwendungen …, 2019 | 13 | 2019 |
projection-to-projection translation for Hybrid X-ray and Magnetic Resonance imaging B Stimpel, C Syben, T Würfl, K Breininger, P Hoelter, A Dörfler, A Maier Scientific Reports 9 (1), 18814, 2019 | 12 | 2019 |