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Gregor Koehler
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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
8322018
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
492023
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
482023
batchgenerators—a python framework for data augmentation
F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ...
Zenodo 3632567, 2020
452020
Sample-efficient automated deep reinforcement learning
JKH Franke, G Köhler, A Biedenkapp, F Hutter
arXiv preprint arXiv:2009.01555, 2020
362020
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
292023
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
242022
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
242022
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
231809
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
222021
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
152020
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
112023
Medical out-of-distribution analysis challenge 2022
D Zimmerer, J Petersen, G Köhler, P Jäger, P Full, T Roß, T Adler, ...
Publisher: Zenodo, 2021
112021
Medical out-of-distribution analysis challenge
D Zimmerer, J Petersen, G Köhler, P Jäger, P Full, T Roß, T Adler, ...
Zenodo, 2020
112020
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
92021
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
91809
MNIST handwritten digit recognition in pytorch
G Koehler
nextjournal. com/gkoehler/pytorch-mnist, 2020
72020
Neural architecture evolution in deep reinforcement learning for continuous control
JKH Franke, G Köhler, N Awad, F Hutter
arXiv preprint arXiv:1910.12824, 2019
72019
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
52023
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