| A generalized network for MRI intensity normalization A Simkó, T Löfstedt, A Garpebring, T Nyholm, J Jonsson arXiv preprint arXiv:1909.05484, 2019 | 14 | 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 | 12 | 2022 |
| 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 | 10 | 2024 |
| Towards MR contrast independent synthetic CT generation A Simkó, M Bylund, G Jönsson, T Löfstedt, A Garpebring, T Nyholm, ... Zeitschrift für Medizinische Physik 34 (2), 270-277, 2024 | 3 | 2024 |
| 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 | 3 | 2019 |
| 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 |
| ProstateZones–Segmentations of the prostatic zones and urethra for the PROSTATEx dataset W Holmlund, A Simkó, K Söderkvist, P Palásti, S Tótin, K Kalmár, ... Scientific Data 11 (1), 1097, 2024 | 2 | 2024 |
| 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 Univ. Minn. Libr 10 (548719.073), 2019 | 2 | 2019 |
| From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning S Ghanbari Azar, L Tronchin, A Simkó, T Nyholm, T Löfstedt Transactions on Machine Learning Research, 2025 | | 2025 |
| Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system G Heilemann, L Zimmermann, T Nyholm, A Simkó, J Widder, G Goldner, ... Physics and Imaging in Radiation Oncology 33, 100724, 2025 | | 2025 |
| 663: Reproducibility guideline for deep learning research in medical imaging A Simkó, A Garpebring, J Jonsson, T Nyholm, T Löfstedt Radiotherapy and Oncology 194, S4424-S4425, 2024 | | 2024 |
| 2191: Automatic segmentation of the prostate, urethra, and prostatic zones W Holmlund, A Simkó, K Söderkvist, P Palásti, S Tótin, K Kalmár, ... Radiotherapy and Oncology 194, S3095-S3099, 2024 | | 2024 |
| 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), 1-14, 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 |
| From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning SG Azar, L Tronchin, A Simkó, T Nyholm, T Löfstedt Transactions on Machine Learning Research, 0 | | |
