| Deep learning for photoacoustic tomography from sparse data S Antholzer, M Haltmeier, J Schwab Inverse problems in science and engineering 27 (7), 987-1005, 2019 | 280 | 2019 |
| NETT: Solving inverse problems with deep neural networks H Li, J Schwab, S Antholzer, M Haltmeier Inverse Problems 36 (6), 065005, 2020 | 260 | 2020 |
| Deep null space learning for inverse problems: convergence analysis and rates J Schwab, S Antholzer, M Haltmeier Inverse Problems 35 (2), 025008, 2019 | 113 | 2019 |
| Photoacoustic image reconstruction via deep learning S Antholzer, M Haltmeier, R Nuster, J Schwab Photons plus ultrasound: Imaging and sensing 2018 10494, 433-442, 2018 | 63 | 2018 |
| A joint deep learning approach for automated liver and tumor segmentation N Gruber, S Antholzer, W Jaschke, C Kremser, M Haltmeier 2019 13th International conference on Sampling Theory and Applications …, 2019 | 55 | 2019 |
| Real-time photoacoustic projection imaging using deep learning J Schwab, S Antholzer, R Nuster, M Haltmeier arXiv preprint arXiv:1801.06693, 2018 | 54* | 2018 |
| NETT regularization for compressed sensing photoacoustic tomography S Antholzer, J Schwab, J Bauer-Marschallinger, P Burgholzer, ... Photons Plus Ultrasound: Imaging and Sensing 2019 10878, 272-282, 2019 | 41 | 2019 |
| Deep learning of truncated singular values for limited view photoacoustic tomography J Schwab, S Antholzer, R Nuster, G Paltauf, M Haltmeier Photons Plus Ultrasound: Imaging and Sensing 2019 10878, 254-262, 2019 | 27 | 2019 |
| Deep Learning Versus -Minimization for Compressed Sensing Photoacoustic Tomography S Antholzer, J Schwab, M Haltmeier 2018 IEEE International Ultrasonics Symposium (IUS), 206-212, 2018 | 22 | 2018 |
| Big in Japan: Regularizing networks for solving inverse problems J Schwab, S Antholzer, M Haltmeier Journal of mathematical imaging and vision 62 (3), 445-455, 2020 | 21 | 2020 |
| Learned backprojection for sparse and limited view photoacoustic tomography J Schwab, S Antholzer, M Haltmeier Photons Plus Ultrasound: Imaging and Sensing 2019 10878, 263-271, 2019 | 21 | 2019 |
| Discretization of learned NETT regularization for solving inverse problems S Antholzer, M Haltmeier Journal of Imaging 7 (11), 239, 2021 | 10 | 2021 |
| Photons Plus Ultrasound: Imaging and Sensing 2018 S Antholzer, M Haltmeier, R Nuster, J Schwab San Francisco, USA U 104944, 2018 | 9 | 2018 |
| Hybrid immediacy: designing with artificial neural networks through physical concept modelling M Bank, V Sandor, R Kraft, S Antholzer, M Berger, T Fabini, B Kovacs, ... Design Modelling Symposium Berlin, 13-23, 2022 | 4 | 2022 |
| Compressive time-of-flight 3D imaging using block-structured sensing matrices S Antholzer, C Wolf, M Sandbichler, M Dielacher, M Haltmeier Inverse Problems 35 (4), 045004, 2019 | 4* | 2019 |
| Compressive time-of-flight imaging S Antholzer, C Wolf, M Sandbichler, M Dielacher, M Haltmeier 2017 International Conference on Sampling Theory and Applications (SampTA …, 2017 | 3 | 2017 |
| Deep Learning for Image Reconstruction M Haltmeier, S Antholzer, J Schwab World Scientific Publishing, 2023 | 2 | 2023 |
| Cluster-Based Autoencoders for Volumetric Point Clouds S Antholzer, M Berger, T Hell arXiv preprint arXiv:2211.01009, 2022 | | 2022 |
| Correction to: Hybrid Immediacy: Designing with Artificial Neural Networks Through Physical Concept Modelling M Bank, V Sandor, R Kraft, S Antholzer, M Berger, T Fabini, B Kovacs, ... Design Modelling Symposium Berlin, C1-C1, 2022 | | 2022 |
| Sampling and resolution in sparse view photoacoustic tomography M Haltmeier, D Obmann, F Dreier, S Antholzer, K Felbermayer, ... European Conference on Biomedical Optics, ES2C. 2, 2021 | | 2021 |
Markus HaltmeierProfessor of Mathematics, University of InnsbruckVerified email at uibk.ac.at
