Paul F. Jaeger
Paul F. Jaeger
Interactive Machine Learning Research Group at DKFZ
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
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
batchgenerators—a python framework for data augmentation
F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ...
Zenodo, 3632567, 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
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
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
Metrics reloaded: Pitfalls and recommendations for image analysis validation
L Maier-Hein, B Menze
arXiv. org, 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
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
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
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
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
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
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