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...

Celý popis

Podrobná bibliografia

Vydané v:	Computer methods and programs in biomedicine Ročník 209; s. 106312
Hlavní autori:	Pastor-Serrano, Oscar, Lathouwers, Danny, Perkó, Zoltán
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.09.2021
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!