TY - JOUR
T1 - Application of GANs for Augmentation of the Mammography Repository
AU - Reyes-Hernández, Yaneth
AU - Perez-Tellez, Fernando
AU - Ruiz-Vanoye, Jorge A.
AU - Flores, Jazmín Rodríguez
AU - Acevedo, Eric Simancas
AU - Diaz-Parra, Ocotlán
AU - Ortiz, Jaime Aguilar
AU - Trejo-Macotela, Francisco Rafael
N1 - Publisher Copyright:
© 2025 Instituto Politecnico Nacional. All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - GAN
KW - generative AI
KW - mammograms
KW - synthetic data
KW - synthetic images
UR - https://www.scopus.com/pages/publications/105010725182
U2 - 10.13053/CyS-29-2-5664
DO - 10.13053/CyS-29-2-5664
M3 - Article
AN - SCOPUS:105010725182
SN - 1405-5546
VL - 29
SP - 893
EP - 900
JO - Computacion y Sistemas
JF - Computacion y Sistemas
IS - 2
ER -
