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01962nab a2200589 i 4500 |
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publ29344 |
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20240119214118.0 |
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240119s2023 hu o 0|| Angol d |
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|a 2296-424X
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7 |
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|a 10.3389/fphy.2023.1236792
|2 doi
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|a 34145498
|2 mtmt
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|a SZTE Publicatio Repozitórium
|b hun
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|a Angol
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|a Czipczer Vanda
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|a Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning
|h [elektronikus dokumentum] /
|c Czipczer Vanda
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|c 2023
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|a 17
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|a FRONTIERS IN PHYSICS
|v 11
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|a Klinikai orvostan
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|a Kolozsvári Bernadett
|e aut
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|a Deák-Karancsi Borbála
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|a Capala E. Marta
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|a Pearson Rachel A.
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|a Borzási Emőke
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|a Együd Zsófia
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|a Gaál Szilvia
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|a Kelemen Gyöngyi
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|a Kószó Renáta Lilla
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|a Paczona Viktor Róbert
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|a Végváry Zoltán
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|a Karancsi Zsófia
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|a Kékesi Ádám
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|a Czunyi Edina
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|a Irmai H. Blanka
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|a Keresnyei G. Nóra
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|a Nagypál Petra
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|a Czabány Renáta
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|a Gyalai Bence
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|a Tass P. Bulcsú
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|a Cziria Balázs
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|a Cozzini Cristina
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|a Estkowsky Lloyd
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|a Ferenczi Lehel
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|a Frontó András
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|a Maxwell Ross
|e aut
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|a Megyeri István
|e aut
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|a Mian Michael
|e aut
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|a Tan Tao
|e aut
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|a Hideghéty Katalin
|e aut
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|a Ruskó László
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|a et al.
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|u http://publicatio.bibl.u-szeged.hu/29344/1/fphy-11-1236792.pdf
|z Dokumentum-elérés
|