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pro vyhledávání: '"Roberto Paolella"'
Autor:
Maria Ines Meyer, Ezequiel de la Rosa, Nuno Pedrosa de Barros, Roberto Paolella, Koen Van Leemput, Diana M. Sima
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, sca
Externí odkaz:
https://doaj.org/article/5f0d2a26ae5f4c04804acaa7faed7b04
Autor:
Maíra Siqueira Pinto, Roberto Paolella, Thibo Billiet, Pieter Van Dyck, Pieter-Jan Guns, Ben Jeurissen, Annemie Ribbens, Arnold J. den Dekker, Jan Sijbers
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisi
Externí odkaz:
https://doaj.org/article/9ded15a2b14147b3a575ce833d8a7b27
Autor:
Roberto Paolella, Nuno Pedrosa de Barros, Maria Ines Meyer, Ezequiel de la Rosa, Koen Van Leemput, Diana M. Sima
Publikováno v:
ISBI
Proceedings
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Proceedings
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We propose a novel data augmentation app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3aa4b597e84ad04cab40fc7c46c588b8
https://doi.org/10.1109/isbi48211.2021.9433833
https://doi.org/10.1109/isbi48211.2021.9433833