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Erich Kobler
Erich Kobler
PostDoc at Institute of Computer Graphics, JKU
Verified email at jku.at
Title
Cited by
Cited by
Year
Learning a variational network for reconstruction of accelerated MRI data
K Hammernik, T Klatzer, E Kobler, MP Recht, DK Sodickson, T Pock, ...
Magnetic resonance in medicine 79 (6), 3055-3071, 2018
9502018
Assessment of the generalization of learned image reconstruction and the potential for transfer learning
F Knoll, K Hammernik, E Kobler, T Pock, MP Recht, DK Sodickson
Magnetic resonance in medicine 81 (1), 116-128, 2019
1322019
Variational networks: connecting variational methods and deep learning
E Kobler, T Klatzer, K Hammernik, T Pock
German conference on pattern recognition, 281-293, 2017
1062017
Total deep variation for linear inverse problems
E Kobler, A Effland, K Kunisch, T Pock
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
392020
Variational networks: An optimal control approach to early stopping variational methods for image restoration
A Effland, E Kobler, K Kunisch, T Pock
Journal of mathematical imaging and vision 62 (3), 396-416, 2020
26*2020
Total Deep Variation: A Stable Regularization Method for Inverse Problems
E Kobler, A Effland, K Kunisch, T Pock
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
13*2021
Bayesian uncertainty estimation of learned variational MRI reconstruction
D Narnhofer, A Effland, E Kobler, K Hammernik, F Knoll, T Pock
IEEE Transactions on Medical Imaging, 2021
122021
Variational adversarial networks for accelerated MR image reconstruction
K Hammernik, E Kobler, T Pock, MP Recht, DK Sodickson, F Knoll
Joint Annual Meeting ISMRM-ESMRMB, 2018
102018
Variational deep learning for low-dose computed tomography
E Kobler, M Muckley, B Chen, F Knoll, K Hammernik, T Pock, D Sodickson, ...
2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018
92018
Accelerating prostate diffusion-weighted MRI using a guided denoising convolutional neural network: retrospective feasibility study
EA Kaye, EA Aherne, C Duzgol, I Häggström, E Kobler, Y Mazaheri, ...
Radiology: Artificial Intelligence 2 (5), e200007, 2020
72020
SparseCT: System concept and design of multislit collimators
B Chen, E Kobler, MJ Muckley, AD Sodickson, T O'Donnell, T Flohr, ...
Medical physics 46 (6), 2589-2599, 2019
72019
Image morphing in deep feature spaces: theory and applications
A Effland, E Kobler, T Pock, M Rajković, M Rumpf
Journal of mathematical imaging and vision 63 (2), 309-327, 2021
52021
Joint multi-anatomy training of a variational network for reconstruction of accelerated magnetic resonance image acquisitions
PM Johnson, MJ Muckley, M Bruno, E Kobler, K Hammernik, T Pock, ...
International Workshop on Machine Learning for Medical Image Reconstruction …, 2019
42019
Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data
A Effland, E Kobler, A Brandenburg, T Klatzer, L Neuhäuser, M Hölzel, ...
International journal of computer assisted radiology and surgery 14 (4), 587-599, 2019
42019
Dynamic multicoil reconstruction using variational networks
K Hammernik, M Schloegl, E Kobler, R Stollberger, T Pock
Proc. ISMRM 27th Annu. Meeting exhibit, 4656, 2019
32019
Variational networks for joint image reconstruction and classification of tumor immune cell interactions in melanoma tissue sections
A Effland, M Hölzel, T Klatzer, E Kobler, J Landsberg, L Neuhäuser, ...
Bildverarbeitung für die Medizin 2018, 334-340, 2018
32018
Trainable regularization for multi-frame superresolution
T Klatzer, D Soukup, E Kobler, K Hammernik, T Pock
German Conference on Pattern Recognition, 90-100, 2017
32017
Time discrete geodesics in deep feature spaces for image morphing
A Effland, E Kobler, T Pock, M Rumpf
International Conference on Scale Space and Variational Methods in Computer …, 2019
22019
Shared prior learning of energy-based models for image reconstruction
T Pinetz, E Kobler, T Pock, A Effland
SIAM Journal on Imaging Sciences 14 (4), 1706-1748, 2021
12021
Analysis of the influence of deviations between training and test data in learned image reconstruction
F Knoll, K Hammernik, E Kobler, T Pock, DK Sodickson, MP Recht
ISMRM Workshop on Machine Learning, 2018
12018
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Articles 1–20