Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies

Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies

Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, particularly for unsupervised learning. This systematic review analyzes GAN applications in healthcare, focusing on image and signal-based studies across various clinical domains. Following Preferred R...

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Bibliographic Details
Main Authors: Muhammed Halil Akpinar, Abdulkadir Sengur, Massimo Salvi, Silvia Seoni, Oliver Faust, Hasan Mir, Filippo Molinari, U. Rajendra Acharya
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Generative adversarial networks (GANs)
medical imaging
data generation
signal simulation
deep learning
Online Access:https://ieeexplore.ieee.org/document/10770591/
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https://ieeexplore.ieee.org/document/10770591/

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