| 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 | 560 | 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 | 82 | 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 | 67 | 2017 |
| 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, 1-21, 2020 | 7* | 2020 |
| 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, 2018 | 7 | 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 | 6 | 2018 |
| Total deep variation: A stable regularizer for inverse problems E Kobler, A Effland, K Kunisch, T Pock arXiv preprint arXiv:2006.08789, 2020 | 4 | 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 | 4 | 2019 |
| 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 | 3 | 2020 |
| 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 | 3 | 2019 |
| 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 |
| 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 | 2 | 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, 1-19, 2020 | 1 | 2020 |
| 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 |
| 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 | 1 | 2018 |
| Shared Prior Learning of Energy-Based Models for Image Reconstruction T Pinetz, E Kobler, T Pock, A Effland arXiv preprint arXiv:2011.06539, 2020 | | 2020 |
| 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 | | 2020 |
| Effect of Multislit Collimator Motion On SparseCT Image Quality for Low-Dose CT Examinations B Chen, E Kobler, T Allmendinger, A Sodickson, D Sodickson, R Otazo Medical Physics, E504-E505, 2019 | | 2019 |
| Learning variational models for blind image deconvolution E Kobler Graz University of Technology, 2014 | | 2014 |
Thomas PockProfessor of Computer Science, TU GrazBestätigte E-Mail-Adresse bei icg.tugraz.at
