Abstract
Generative adversarial networks (GANs) offer an innovative approach to synthetic image generation. They have significantly impacted the creation of images that would otherwise be difficult to obtain. In this study, we examine several GAN architectures to determine whether they can generate synthetic mammography images to enrich an existing repository, thereby improving AI training for breast-cancer detection and supporting research into this disease with a more diverse dataset.
Keywords
GAN, generative AI, mammograms, synthetic data, synthetic images
DOI Link
Publication Date
2025-01-01
Publication Title
Computacion y Sistemas
Volume
29
Issue
2
ISSN
1405-5546
First Page
893
Last Page
900
Deposit Date
2026-11-13
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Additional Links
Recommended Citation
Reyes-Hernández, Y., Perez-Tellez, F., Ruiz-Vanoye, J., Flores, J., Acevedo, E., Diaz-Parra, O., Ortiz, J., & Trejo-Macotela, F. (2025) 'Application of GANs for Augmentation of the Mammography Repository', Computacion y Sistemas, 29(2), pp. 893-900. Available at: 10.13053/CyS-29-2-5664