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Umeå University
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Staff photo Attila Simko

Attila Simko

Contact

E-mail
attila.simko@umu.se
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Works as

Affiliation
Staff scientist at Department of Diagnostics and Intervention
Location
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From promise to practice: a study of common pitfalls behind the generalization gap in machine learning
Transactions on Machine Learning Research
Ghanbari Azar, Saeideh; Tronchin, Lorenzo; Simkó, Attila; et al.
Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system
Physics and Imaging in Radiation Oncology, Elsevier 2025, Vol. 33
Heilemann, Gerd; Zimmermann, Lukas; Nyholm, Tufve; et al.
ProstateZones: segmentations of the prostatic zones and urethra for the PROSTATEx dataset
Scientific Data, Springer Nature 2024, Vol. 11, (1)
Holmlund, William; Simkó, Attila; Söderkvist, Karin; et al.
Towards MR contrast independent synthetic CT generation
Zeitschrift für Medizinische Physik, Elsevier 2024, Vol. 34, (2) : 270-277
Simkó, Attila; Bylund, Mikael; Jönsson, Gustav; et al.
Contributions to deep learning for imaging in radiotherapy
Umeå University medical dissertations, 2264
Simkó, Attila
Reproducibility of the methods in medical imaging with deep learning
Medical imaging with deep learning 2023, ML Research Press 2023 : 95-106
Simkó, Attila; Garpebring, Anders; Jonsson, Joakim; et al.
Improving MR image quality with a multi-task model, using convolutional losses
BMC Medical Imaging, BioMed Central (BMC) 2023, Vol. 23, (1)
Simkó, Attila; Ruiter, Simone; Löfstedt, Tommy; et al.
MRI bias field correction with an implicitly trained CNN
Proceedings of the 5th international conference on medical imaging with deep learning, ML Research Press 2022 : 1125-1138
Simkó, Attila; Löfstedt, Tommy; Garpebring, Anders; et al.
Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR, Lübeck University; Hamburg University of Technology 2021 : 713-727
Simkó, Attila; Löfstedt, Tommy; Garpebring, Anders; et al.
Localization Network and End-to-End Cascaded U-Nets for Kidney Tumor Segmentation
MICCAI 2019, Shenzhen, China, Oct 13-17, 2019
Vu, Minh; Grimbergen, Guus; Simkó, Attila; et al.
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