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

AKPINAR M. H., Sengur A., Salvi M., Seoni S., Faust O., Mir H., ...Daha Fazla

IEEE Open Journal of Engineering in Medicine and Biology, 2024 (ESCI, Scopus) identifier

  • Yayın Türü: Makale / Derleme
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/ojemb.2024.3508472
  • Dergi Adı: IEEE Open Journal of Engineering in Medicine and Biology
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, BIOSIS, Compendex, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: data generation, deep learning, Generative adversarial networks, medical imaging, signal simulation
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

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 Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 72 relevant journal articles. Our findings reveal that magnetic resonance imaging (MRI) and electrocardiogram (ECG) signal acquisition techniques were most utilized, with brain studies (22%), cardiology (18%), cancer (15%), ophthalmology (12%), and lung studies (10%) being the most researched areas. We discuss key GAN architectures, including cGAN (31%) and CycleGAN (18%), along with datasets, evaluation metrics, and performance outcomes. The review highlights promising data augmentation, anonymization, and multi-task learning results. We identify current limitations, such as the lack of standardized metrics and direct comparisons, and propose future directions, including the development of no-reference metrics, immersive simulation scenarios, and enhanced interpretability.