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 | 877 | 2018 |
Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model S Roy, T Wald, G Koehler, MR Rokuss, N Disch, J Holzschuh, D Zimmerer, ... arXiv preprint arXiv:2304.05396, 2023 | 56 | 2023 |
Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge J Ma, Y Zhang, S Gu, C Ge, S Ma, A Young, C Zhu, K Meng, X Yang, ... arXiv preprint arXiv:2308.05862, 2023 | 53 | 2023 |
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 | 46 | 2023 |
batchgenerators—a python framework for data augmentation F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... Zenodo 3632567, 2020 | 46 | 2020 |
Sample-efficient automated deep reinforcement learning JKH Franke, G Köhler, A Biedenkapp, F Hutter arXiv preprint arXiv:2009.01555, 2020 | 37 | 2020 |
Deep learning–based assessment of oncologic outcomes from natural language processing of structured radiology reports MA Fink, K Kades, A Bischoff, M Moll, M Schnell, M Küchler, G Köhler, ... Radiology: Artificial Intelligence 4 (5), e220055, 2022 | 29 | 2022 |
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 | 27 | 2022 |
nnU-Net: self-adapting framework for U-Net-based medical image segmentation. 2018 F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... arXiv preprint arXiv:1809.10486, 1809 | 26 | 1809 |
Adapting bidirectional encoder representations from transformers (BERT) to assess clinical semantic textual similarity: algorithm development and validation study K Kades, J Sellner, G Koehler, PM Full, TYE Lai, J Kleesiek, ... JMIR medical informatics 9 (2), e22795, 2021 | 22 | 2021 |
batchgenerators-a python framework for data augmentation (2020) F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... DOI: https://doi. org/10.5281/zenodo 3632567, 2020 | 15 | 2020 |
Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model T Wald, S Roy, G Koehler, N Disch, MR Rokuss, J Holzschuh, D Zimmerer, ... Medical Imaging with Deep Learning, short paper track, 2023 | 13 | 2023 |
Medical out-of-distribution analysis challenge 2022 D Zimmerer, J Petersen, G Köhler, P Jäger, P Full, T Roß, T Adler, ... Publisher: Zenodo, 2021 | 11 | 2021 |
Medical out-of-distribution analysis challenge D Zimmerer, J Petersen, G Köhler, P Jäger, P Full, T Roß, T Adler, ... Zenodo, 2020 | 11 | 2020 |
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 |
nnU-Net: self-adapting framework for U-Net-based medical image segmentation. CoRR abs/1809.10486 (2018) F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... arXiv preprint arXiv:1809.10486, 1809 | 9 | 1809 |
MNIST handwritten digit recognition in pytorch G Koehler nextjournal. com/gkoehler/pytorch-mnist, 2020 | 8 | 2020 |
Neural architecture evolution in deep reinforcement learning for continuous control JKH Franke, G Köhler, N Awad, F Hutter arXiv preprint arXiv:1910.12824, 2019 | 8 | 2019 |
Medical out-of-distribution analysis challenge (Mar 2020) D Zimmerer, J Petersen, G Köhler, P Jäger, P Full, T Roß, T Adler, ... URL https://doi. org/10.5281/zenodo 3784230, 0 | 7 | |
Cradl: Contrastive representations for unsupervised anomaly detection and localization CT Lüth, D Zimmerer, G Koehler, PF Jaeger, F Isensee, J Petersen, ... arXiv preprint arXiv:2301.02126, 2023 | 5 | 2023 |