| 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 | 4223* | 2021 |
| 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 | 932 | 2023 |
| 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 | 347 | 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 | 145 | 2021 |
| 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 | 144 | 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 | 133 | 2018 |
| 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 | 67 | 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 | 38 | 2019 |
| Metrics reloaded: recommendations for image analysis validation L Maier-Hein, A Reinke, P Godau, MD Tizabi, F Buettner, E Christodoulou, ... Nature methods, 1-18, 2024 | 36 | 2024 |
| Mednext: transformer-driven scaling of convnets for medical image segmentation S Roy, G Koehler, C Ulrich, M Baumgartner, J Petersen, F Isensee, ... International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 34 | 2023 |
| 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 | 27 | 2020 |
| 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 | 25 | 2022 |
| 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 | 25 | 2016 |
| 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 | 24 | 2018 |
| Understanding metric-related pitfalls in image analysis validation A Reinke, MD Tizabi, M Baumgartner, M Eisenmann, D Heckmann-Nötzel, ... Nature methods, 1-13, 2024 | 21 | 2024 |
| Common limitations of performance metrics in biomedical image analysis A Reinke, M Eisenmann, MD Tizabi, CH Sudre, T Rädsch, M Antonelli, ... Medical imaging with deep learning, 2021 | 14 | 2021 |
| A residual diffusion model for high perceptual quality codec augmentation NF Ghouse, J Petersen, A Wiggers, T Xu, G Sautiere arXiv preprint arXiv:2301.05489, 2023 | 11* | 2023 |
| Telestration with augmented reality for visual presentation of intraoperative target structures in minimally invasive surgery: a randomized controlled study C Wild, F Lang, AS Gerhäuser, MW Schmidt, KF Kowalewski, J Petersen, ... Surgical Endoscopy 36 (10), 7453-7461, 2022 | 11 | 2022 |
| Metrics reloaded-a new recommendation framework for biomedical image analysis validation A Reinke, L Maier-Hein, E Christodoulou, B Glocker, P Scholz, F Isensee, ... Medical imaging with deep learning, 2022 | 11 | 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 | 9 | 2021 |
