Cargando…
Deploying deep learning models on unseen medical imaging using adversarial domain adaptation
The fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 c...
Autores principales: | Valliani, Aly A., Gulamali, Faris F., Kwon, Young Joon, Martini, Michael L., Wang, Chiatse, Kondziolka, Douglas, Chen, Viola J., Wang, Weichung, Costa, Anthony B., Oermann, Eric K. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565422/ https://www.ncbi.nlm.nih.gov/pubmed/36240135 http://dx.doi.org/10.1371/journal.pone.0273262 |
Ejemplares similares
-
Deep Learning and Neurology: A Systematic Review
por: Valliani, Aly Al-Amyn, et al.
Publicado: (2019) -
Visual state estimation in unseen environments through domain adaptation and metric learning
por: Güler, Püren, et al.
Publicado: (2022) -
Semi-supervised adversarial discriminative domain adaptation
por: Nguyen, Thai-Vu, et al.
Publicado: (2022) -
Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
por: Martini, Michael L., et al.
Publicado: (2021) -
Simultaneously Improve Transferability and Discriminability for Adversarial Domain Adaptation
por: Xiao, Ting, et al.
Publicado: (2021)