| A generalized network for MRI intensity normalization A Simkó, T Löfstedt, A Garpebring, T Nyholm, J Jonsson arXiv preprint arXiv:1909.05484, 2019 | 12 | 2019 |
| MRI bias field correction with an implicitly trained CNN A Simkó, T Löfstedt, A Garpebring, T Nyholm, J Jonsson International Conference on Medical Imaging with Deep Learning, 1125-1138, 2022 | 6 | 2022 |
| End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation MH Vu, G Grimbergen, A Simkó, T Nyholm, T Löfstedt arXiv preprint arXiv:1910.07521, 2019 | 4 | 2019 |
| Reproducibility of the Methods in Medical Imaging with Deep Learning. A Simkó, A Garpebring, J Jonsson, T Nyholm, T Löfstedt Medical Imaging with Deep Learning, 95-106, 2024 | 3 | 2024 |
| Comparative testing of dark matter models with 15 HSB and 15 LSB galaxies E Kun, Z Keresztes, A Simkó, G Szűcs, LÁ Gergely Astronomy & Astrophysics 608, A42, 2017 | 3 | 2017 |
| Changing the contrast of magnetic resonance imaging signals using deep learning AT Simko, T Löfstedt, A Garpebring, M Bylund, T Nyholm, J Jonsson Medical Imaging with Deep Learning, 713-727, 2021 | 2 | 2021 |
| Localization Network and End-to-End Cascaded U-Nets for Kidney Tumor Segmentation MH Vu, G Grimbergen, A Simkó, T Nyholm, T Löfstedt University of Minnesota Libraries Publishing, 2019 | 2 | 2019 |
| Towards MR contrast independent synthetic CT generation A Simkó, M Bylund, G Jönsson, T Löfstedt, A Garpebring, T Nyholm, ... Zeitschrift für Medizinische Physik, 2023 | 1 | 2023 |
| Improving MR image quality with a multi-task model, using convolutional losses A Simkó, S Ruiter, T Löfstedt, A Garpebring, T Nyholm, M Bylund, ... BMC Medical Imaging 23 (1), 148, 2023 | | 2023 |
| PO-1698 Towards MR contrast independent synthetic CT generation. A Simko, M Bylund, G Jönsson, T Löfstedt, A Garpebring, T Nyholm, ... Radiotherapy and Oncology 182, S1409-S1410, 2023 | | 2023 |
| Contributions to deep learning for imaging in radiotherapy A Simkó Umeå University, 2023 | | 2023 |
