How Do Generative Adversarial Networks Improve Medical Imaging?
Keywords:
Generative Contradictory Network (GAN), Clinical Applications, Medical ImagingAbstract
The generative adversarial network (GAN) has proven to be a transformative tool in medical imaging and has solved challenges in image synthesis, growth, division and reconstruction. By taking advantage of a unique side effect mechanism, GAN produces high-quality synthetic data and processes imaging outputs for clinical applications. This article examines the features and applications of GAN in medical imaging, which emphasises their ability to increase clinical accuracy, reduce imaging costs and data on data, and address limitations such as training instability and moral concerns, removing deficiency.
DOI: https://doi.org/10.24321/2455.3190.202503
References
Chakraborty T, KS UR, Naik SM, Panja M, Manvitha B. Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art. Machine Learning: Science and Technology. 2024 Jan 29;5(1):011001.
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). “Unpaired Image-to-Image Translation Using Cycle- Consistent Adversarial Networks.” IEEE International Conference on Computer Vision.
Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Change Loy C. Esrgan: Enhanced super-resolution generative adversarial networks. InProceedings of the European conference on computer vision (ECCV) workshops 2018 (pp. 0-0).
