| 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 | 950 | 2018 |
| 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 | 132 | 2019 |
| Variational networks: connecting variational methods and deep learning E Kobler, T Klatzer, K Hammernik, T Pock German conference on pattern recognition, 281-293, 2017 | 106 | 2017 |
| 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 | 39 | 2020 |
| 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 | 12 | 2021 |
| 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 | 10 | 2018 |
| 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 | 9 | 2018 |
| 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 | 7 | 2020 |
| 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 | 7 | 2019 |
| 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 | 5 | 2021 |
| 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 | 4 | 2019 |
| 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 | 4 | 2019 |
| Dynamic multicoil reconstruction using variational networks K Hammernik, M Schloegl, E Kobler, R Stollberger, T Pock Proc. ISMRM 27th Annu. Meeting exhibit, 4656, 2019 | 3 | 2019 |
| 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 | 3 | 2018 |
| Trainable regularization for multi-frame superresolution T Klatzer, D Soukup, E Kobler, K Hammernik, T Pock German Conference on Pattern Recognition, 90-100, 2017 | 3 | 2017 |
| 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 | 2 | 2019 |
| 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 | 1 | 2021 |
| 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 | 1 | 2018 |
Thomas PockProfessor of Computer Science, TU GrazVerified email at icg.tugraz.at
