| nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation F Isensee, PF Jaeger, SAA Kohl, J Petersen, KH Maier-Hein Nature methods 18 (2), 203-211, 2021 | 1291 | 2021 |
| nnu-net: Self-adapting framework for u-net-based medical image segmentation F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... arXiv preprint arXiv:1809.10486, 2018 | 611 | 2018 |
| Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study P Kickingereder, F Isensee, I Tursunova, J Petersen, U Neuberger, ... The Lancet Oncology 20 (5), 728-740, 2019 | 250 | 2019 |
| Automated design of deep learning methods for biomedical image segmentation F Isensee, PF Jäger, SAA Kohl, J Petersen, KH Maier-Hein arXiv preprint arXiv:1904.08128, 2019 | 176 | 2019 |
| nnU-Net: Breaking the Spell on Successful Medical Image Segmentation F Isensee, J Petersen, SAA Kohl, PF Jäger, KH Maier-Hein arXiv preprint arXiv:1904.08128, 2019 | 166 | 2019 |
| Context-encoding variational autoencoder for unsupervised anomaly detection D Zimmerer, SAA Kohl, J Petersen, F Isensee, KH Maier-Hein arXiv preprint arXiv:1812.05941, 2018 | 94 | 2018 |
| Unsupervised anomaly localization using variational auto-encoders D Zimmerer, F Isensee, J Petersen, S Kohl, K Maier-Hein Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 92 | 2019 |
| 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 | 77 | 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 |
| Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study CJ Preetha, H Meredig, G Brugnara, MA Mahmutoglu, M Foltyn, F Isensee, ... The Lancet Digital Health 3 (12), e784-e794, 2021 | 24 | 2021 |
| Virtual raters for reproducible and objective assessments in radiology J Kleesiek, J Petersen, M Döring, K Maier-Hein, U Köthe, W Wick, ... Scientific reports 6 (1), 25007, 2016 | 20 | 2016 |
| Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis G Brugnara, F Isensee, U Neuberger, D Bonekamp, J Petersen, R Diem, ... European Radiology 30, 2356-2364, 2020 | 16 | 2020 |
| A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients D Zimmerer, J Petersen, SAA Kohl, KH Maier-Hein Medical Imaging meets NeurIPS 2018, 2018 | 16 | 2018 |
| Common limitations of performance metrics in biomedical image analysis A Reinke, L Maier-Hein, H Müller Proceedings of the Medical Imaging with Deep Learning (MIDL 2021), 2021 | 7 | 2021 |
| 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 |
| High-and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection D Zimmerer, J Petersen, K Maier-Hein arXiv preprint arXiv:1911.12161, 2019 | 5 | 2019 |
| A software application for interactive medical image segmentation with active user guidance J Petersen, M Bendszus, J Debus, S Heiland, KH Maier-Hein Proc. MICCAI-IMIC, 70-77, 2016 | 4 | 2016 |
| Automated Containerized Medical Image Processing Based on MITK and Python: A Modular System to Implement Medical Image Processing Pipelines and Visualize Meta Data CJ Goch, J Metzger, M Hettich, A Klein, T Norajitra, M Götz, J Petersen, ... Bildverarbeitung für die Medizin 2018: Algorithmen-Systeme-Anwendungen …, 2018 | 3 | 2018 |
| Effective user guidance in online interactive semantic segmentation J Petersen, M Bendszus, J Debus, S Heiland, KH Maier-Hein Medical Imaging 2017: Computer-Aided Diagnosis 10134, 486-493, 2017 | 3 | 2017 |
| GP-ConvCNP: Better generalization for conditional convolutional Neural Processes on time series data J Petersen, G Köhler, D Zimmerer, F Isensee, PF Jäger, KH Maier-Hein Uncertainty in Artificial Intelligence, 939-949, 2021 | 2* | 2021 |
