Deploying deep learning models on unseen medical imaging using adversarial domain adaptation

Deploying deep learning models on unseen medical imaging using adversarial domain adaptation

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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...

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Bibliographic Details
Main Authors: Aly A. Valliani, Faris F. Gulamali, Young Joon Kwon, Michael L. Martini, Chiatse Wang, Douglas Kondziolka, Viola J. Chen, Weichung Wang, Anthony B. Costa, Eric K. Oermann
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565422/?tool=EBI

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565422/?tool=EBI

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