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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Published in:	IEEE transactions on ultrasonics, ferroelectrics, and frequency control Vol. 72; no. 5; pp. 624 - 635
Main Authors:	Fatima, Noreen, Mento, Federico, Afrakhteh, Sajjad, Perrone, Tiziano, Smargiassi, Andrea, Inchingolo, Riccardo, Demi, Libertario
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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 these limitations, this study introduces a novel supervised autoencoder generative adversarial network (SA-GAN) for data augmentation, leveraging advanced generative artificial intelligence (AI) to create high-quality synthetic samples for minority classes. In addition, the traditional data augmentation technique is used for comparison. The SA-GAN incorporates an autoencoder to develop a conditional latent space, effectively addressing weight clipping issues and ensuring higher quality synthetic data. The generated samples are evaluated using similarity metrics and expert analysis to validate their utility. Furthermore, state-of-the-art neural networks are used for multiclass classification, and their performance is compared when trained with GAN-based augmentation versus traditional data augmentation techniques. These contributions enhance the robustness and reliability of AI models in mitigating class imbalance in LUS analysis.
AbstractList	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 these limitations, this study introduces a novel supervised autoencoder generative adversarial network (SA-GAN) for data augmentation, leveraging advanced generative artificial intelligence (AI) to create high-quality synthetic samples for minority classes. In addition, the traditional data augmentation technique is used for comparison. The SA-GAN incorporates an autoencoder to develop a conditional latent space, effectively addressing weight clipping issues and ensuring higher quality synthetic data. The generated samples are evaluated using similarity metrics and expert analysis to validate their utility. Furthermore, state-of-the-art neural networks are used for multiclass classification, and their performance is compared when trained with GAN-based augmentation versus traditional data augmentation techniques. These contributions enhance the robustness and reliability of AI models in mitigating class imbalance in LUS analysis. 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 these limitations, this study introduces a novel supervised autoencoder generative adversarial network (SA-GAN) for data augmentation, leveraging advanced generative AI to create high-quality synthetic samples for minority classes. Additionally, the traditional data augmentation technique is employed for comparison. The SA-GAN incorporates an autoencoder to develop a conditional latent space, effectively addressing weight clipping issues and ensuring higher-quality synthetic data. The generated samples are evaluated using similarity metrics and expert analysis to validate their utility. Furthermore, state-of-the-art neural networks are employed for multi-class classification, and their performance is compared when trained with GAN-based augmentation versus traditional data augmentation techniques. These contributions enhance the robustness and reliability of AI models in mitigating class imbalance in LUS analysis.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 these limitations, this study introduces a novel supervised autoencoder generative adversarial network (SA-GAN) for data augmentation, leveraging advanced generative AI to create high-quality synthetic samples for minority classes. Additionally, the traditional data augmentation technique is employed for comparison. The SA-GAN incorporates an autoencoder to develop a conditional latent space, effectively addressing weight clipping issues and ensuring higher-quality synthetic data. The generated samples are evaluated using similarity metrics and expert analysis to validate their utility. Furthermore, state-of-the-art neural networks are employed for multi-class classification, and their performance is compared when trained with GAN-based augmentation versus traditional data augmentation techniques. These contributions enhance the robustness and reliability of AI models in mitigating class imbalance in LUS analysis.
Author	Afrakhteh, Sajjad Demi, Libertario Smargiassi, Andrea Fatima, Noreen Perrone, Tiziano Inchingolo, Riccardo Mento, Federico
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SubjectTerms	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
Title	Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network
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