| nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation F Isensee*, PF Jaeger*, SAA Kohl, J Petersen, ... Nature methods 18 (2), 203-211, 2021 | 1601* | 2021 |
| Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? O Bernard, A Lalande, C Zotti, F Cervenansky, X Yang, PA Heng, I Cetin, ... IEEE transactions on medical imaging 37 (11), 2514-2525, 2018 | 918 | 2018 |
| nnu-net: Self-adapting framework for u-net-based medical image segmentation F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... MICCAI 2018, Medical Segmentation Decathlon Challenge Entry, 2018 | 611 | 2018 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 559 | 2023 |
| Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features F Isensee*, PF Jaeger*, PM Full, I Wolf, S Engelhardt, ... International workshop on statistical atlases and computational models of …, 2017 | 273 | 2017 |
| Retina U-Net: Embarrassingly simple exploitation of segmentation supervision for medical object detection PF Jaeger, SAA Kohl, S Bickelhaupt, F Isensee, TA Kuder, HP Schlemmer, ... ML4H Workshop, Neurips, 171-183, 2020 | 162 | 2020 |
| nnU-Net for brain tumor segmentation F Isensee, PF Jäger, PM Full, P Vollmuth, KH Maier-Hein Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2021 | 132 | 2021 |
| Common limitations of image processing metrics: A picture story A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ... arXiv preprint arXiv:2104.05642, 2021 | 84 | 2021 |
| Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer S Bickelhaupt*, PF Jaeger*, FB Laun, W Lederer, H Daniel, TA Kuder, ... Radiology 287 (3), 761-770, 2018 | 83 | 2018 |
| batchgenerators—a python framework for data augmentation F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... Zenodo, 3632567, 2020 | 37* | 2020 |
| Studying robustness of semantic segmentation under domain shift in cardiac MRI PM Full, F Isensee, PF Jäger, K Maier-Hein Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC …, 2021 | 32 | 2021 |
| nndetection: A self-configuring method for medical object detection M Baumgartner*, PF Jäger*, F Isensee, ... International Conference on Medical Image Computing and Computer-Assisted …, 2021 | 29 | 2021 |
| Deep probabilistic modeling of glioma growth J Petersen, PF Jäger, F Isensee, SAA Kohl, U Neuberger, W Wick, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 28 | 2019 |
| Metrics reloaded: Pitfalls and recommendations for image analysis validation L Maier-Hein, B Menze arXiv. org, 2022 | 26 | 2022 |
| Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge KM Timmins, IC van der Schaaf, E Bennink, YM Ruigrok, X An, ... Neuroimage 238, 118216, 2021 | 23 | 2021 |
| MONAI: An open-source framework for deep learning in healthcare MJ Cardoso, W Li, R Brown, N Ma, E Kerfoot, Y Wang, B Murrey, ... arXiv preprint arXiv:2211.02701, 2022 | 14 | 2022 |
| Revealing hidden potentials of the q-space signal in breast cancer PF Jäger, S Bickelhaupt, FB Laun, W Lederer, D Heidi, TA Kuder, ... Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 12 | 2017 |
| MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images D Zimmerer, PM Full, F Isensee, P Jäger, T Adler, J Petersen, G Köhler, ... IEEE Transactions on Medical Imaging 41 (10), 2728-2738, 2022 | 7 | 2022 |
| Continuous-time deep glioma growth models J Petersen, F Isensee, G Köhler, PF Jäger, D Zimmerer, U Neuberger, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 6 | 2021 |
| Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection. arXiv 2018 PF Jaeger, SAA Kohl, S Bickelhaupt, F Isensee, TA Kuder, HP Schlemmer, ... arXiv preprint arXiv:1811.08661, 0 | 6 | |
Klaus H. Maier-HeinProfessor, Medical Image Computing, German Cancer Research CenterVerified email at dkfz.de
