A semi-supervised autoencoder framework for joint generation and classification of breathing

•Models of breathing motion using 1D convolutional neural networks and probabilistic autoencoders.•A novel semi-supervised algorithm based on Adversarial Autoencoders that allows joint classification and generation of breathing.•The models are trained with 4% and 12% of the labeled data and achieve...

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Veröffentlicht in:	Computer methods and programs in biomedicine Jg. 209; S. 106312
Hauptverfasser:	Pastor-Serrano, Oscar, Lathouwers, Danny, Perkó, Zoltán
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2021
Schlagworte:	Breathing signals Convolutional neural network Deep learning Probabilistic autoencoder Respiratory motion Semi-supervised learning Deep learning Probabilistic autoencoder Breathing signals Respiratory motion Semi-supervised learning Convolutional neural network
ISSN:	0169-2607, 1872-7565, 1872-7565
Online-Zugang:	Volltext
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