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Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
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Vol. 8, Special Issue 4 (2019)
Innovations in generative adversarial networks (GANs) for synthetic data generation in medical imaging: A review
Author(s):
Dr. Yogesh Bhomia, Dr. Sunil Kumar Mishra and Prabhat Tiwari
Dr. Yogesh Bhomia, Dr. Sunil Kumar Mishra and Prabhat Tiwari
Abstract:
The exponential growth of medical imaging data has significantly propelled advancements in diagnostic and therapeutic strategies, fostering a paradigm shift towards personalized medicine. However, the scarcity of labeled datasets for training deep learning models poses a significant bottleneck in leveraging the full potential of these technologies. Generative Adversarial Networks (GANs) have emerged as a transformative solution, offering the capability to generate realistic synthetic medical images that augment limited datasets.
The exponential growth of medical imaging data has significantly propelled advancements in diagnostic and therapeutic strategies, fostering a paradigm shift towards personalized medicine. However, the scarcity of labeled datasets for training deep learning models poses a significant bottleneck in leveraging the full potential of these technologies. Generative Adversarial Networks (GANs) have emerged as a transformative solution, offering the capability to generate realistic synthetic medical images that augment limited datasets.
This comprehensive review explores the recent innovations in GAN-based approaches for synthetic data generation in medical imaging, focusing on their applications, challenges, and potential impact on healthcare. We delve into the diverse architectures and training strategies employed in the realm of GANs, ranging from traditional architectures to more recent developments, such as progressive growing networks and attention mechanisms.
The review highlights the pivotal role of GANs in addressing data scarcity in medical imaging by producing synthetic datasets that closely mimic the statistical characteristics of real-world data. We discuss the challenges associated with ensuring the clinical relevance and fidelity of synthetic images, emphasizing the importance of domain adaptation techniques and benchmarking against real data.
Furthermore, the review explores the ethical considerations surrounding the use of synthetic data in medical imaging, acknowledging the necessity of transparent reporting and validation methods to build trust in the reliability of GAN-generated datasets. We discuss the ongoing efforts to standardize evaluation metrics for synthetic data quality and emphasize the importance of interdisciplinary collaboration between computer scientists, clinicians, and ethicists in shaping the future of synthetic data generation in healthcare.
Pages: 22-25 | 152 Views 77 Downloads
How to cite this article:
Dr. Yogesh Bhomia, Dr. Sunil Kumar Mishra and Prabhat Tiwari. Innovations in generative adversarial networks (GANs) for synthetic data generation in medical imaging: A review. The Pharma Innovation Journal. 2019; 8(4S): 22-25. DOI: 10.22271/tpi.2019.v8.i4Sa.25254
Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
Main Menu
Special Issue
Copyright Form
Identifier
The Pharma Innovation Journal
