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0567321 - ÚPT 2024 RIV US eng J - Článek v odborném periodiku
Rizzo, R. - Dziadosz, M. - Kyathanahally, S. P. - Shamaei, Amirmohammad - Kreis, R.
Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.
Magnetic Resonance in Medicine. Roč. 89, č. 5 (2023), s. 1707-1727. ISSN 0740-3194. E-ISSN 1522-2594
GRANT EU: European Commission(XE) 813120 - INSPiRE-MED
Institucionální podpora: RVO:68081731
Klíčová slova: active learning * bias * deep learning * ensemble of networks * model fitting * magnetic resonance spectroscopy * quantification
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 3.3, rok: 2022
Způsob publikování: Open access
https://onlinelibrary.wiley.com/doi/10.1002/mrm.29561
Trvalý link: https://hdl.handle.net/11104/0338584
Rizzo, R. - Dziadosz, M. - Kyathanahally, S. P. - Shamaei, Amirmohammad - Kreis, R.
Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.
Magnetic Resonance in Medicine. Roč. 89, č. 5 (2023), s. 1707-1727. ISSN 0740-3194. E-ISSN 1522-2594
GRANT EU: European Commission(XE) 813120 - INSPiRE-MED
Institucionální podpora: RVO:68081731
Klíčová slova: active learning * bias * deep learning * ensemble of networks * model fitting * magnetic resonance spectroscopy * quantification
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 3.3, rok: 2022
Způsob publikování: Open access
https://onlinelibrary.wiley.com/doi/10.1002/mrm.29561
Trvalý link: https://hdl.handle.net/11104/0338584