Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network

Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepresented. Traditional oversampling techniques, which simply duplicate original data, have limited effectiveness in addressing this issue. To overcome t...

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Veröffentlicht in:	IEEE transactions on ultrasonics, ferroelectrics, and frequency control Jg. 72; H. 5; S. 624 - 635
Hauptverfasser:	Fatima, Noreen, Mento, Federico, Afrakhteh, Sajjad, Perrone, Tiziano, Smargiassi, Andrea, Inchingolo, Riccardo, Demi, Libertario
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.05.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial intelligence Autoencoder Autoencoders COVID-19 Data augmentation deep convolutional (DC) discriminator generative adversarial network (GAN) Generative Adversarial Networks Generative artificial intelligence Hospitals Humans Image analysis Image Processing, Computer-Assisted - methods Kernel Lung - diagnostic imaging lung ultrasound (LUS) Lungs Medical imaging multiclass classification Neural networks Neural Networks, Computer Pneumonia supervised autoencoder (SA) Synthetic data Training Ultrasonic imaging Ultrasonography - methods
ISSN:	0885-3010, 1525-8955, 1525-8955
Online-Zugang:	Volltext
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