Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (T...

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Detalles Bibliográficos
Autores principales: Soliman, Amira, Chang, Jose R., Etminani, Kobra, Byttner, Stefan, Davidsson, Anette, Martínez-Sanchis, Begoña, Camacho, Valle, Bauckneht, Matteo, Stegeran, Roxana, Ressner, Marcus, Agudelo-Cifuentes, Marc, Chincarini, Andrea, Brendel, Matthias, Rominger, Axel, Bruffaerts, Rose, Vandenberghe, Rik, Kramberger, Milica G., Trost, Maja, Nicastro, Nicolas, Frisoni, Giovanni B., Lemstra, Afina W., Berckel, Bart N. M. van, Pilotto, Andrea, Padovani, Alessandro, Morbelli, Silvia, Aarsland, Dag, Nobili, Flavio, Garibotto, Valentina, Ochoa-Figueroa, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727842/
https://www.ncbi.nlm.nih.gov/pubmed/36476613
http://dx.doi.org/10.1186/s12911-022-02054-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727842/
https://www.ncbi.nlm.nih.gov/pubmed/36476613
http://dx.doi.org/10.1186/s12911-022-02054-7

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