Application of GANs to Augment the Mammography Repository

Authors

  • Yaneth Reyes-Hernández Universidad Politécnica de Pachuca
  • Fernando Perez-Tellez Technological University of Dublin
  • Jorge A. Ruiz-Vanoye Universidad Politécnica de Pachuca
  • Jazmín Rodríguez Flores Universidad Politécnica de Pachuca
  • Eric Simancas Acevedo Universidad Politécnica de Pachuca
  • Ocotlán Diaz-Parra Universidad Politécnica de Pachuca
  • Jaime Aguilar Ortiz Universidad Politécnica de Pachuca
  • Francisco Rafael Trejo-Macotela Universidad Politécnica de Pachuca

DOI:

https://doi.org/10.13053/cys-29-2-5664

Keywords:

GAN, synthetic data, synthetic images, mammograms, generative AI

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.

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Published

2025-06-28

Issue

Vol. 29 No. 2 (2025): 29(2) 2025

Section

Articles

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