Application of GANs for Augmentation of the Mammography Repository

Yaneth Reyes-Hernández; Fernando Perez-Tellez; Jorge A. Ruiz-Vanoye; Jazmín Rodríguez Flores; Eric Simancas Acevedo; Ocotlán Diaz-Parra; Jaime Aguilar Ortiz; Francisco Rafael Trejo-Macotela

doi:10.13053/CyS-29-2-5664

Application of GANs for Augmentation of the Mammography Repository

Yaneth Reyes-Hernández
, Fernando Perez-Tellez
, Jorge A. Ruiz-Vanoye
, Jazmín Rodríguez Flores
, Eric Simancas Acevedo
, Ocotlán Diaz-Parra
, Jaime Aguilar Ortiz
, Francisco Rafael Trejo-Macotela

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	893-900
Number of pages	8
Journal	Computacion y Sistemas
Volume	29
Issue number	2
DOIs	https://doi.org/10.13053/CyS-29-2-5664
Publication status	Published - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

GAN
generative AI
mammograms
synthetic data
synthetic images

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10.13053/CyS-29-2-5664

Cite this

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title = "Application of GANs for Augmentation of the Mammography Repository",

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",

author = "Yaneth Reyes-Hern{\'a}ndez and Fernando Perez-Tellez and Ruiz-Vanoye, \{Jorge A.\} and Flores, \{Jazm{\'i}n Rodr{\'i}guez\} and Acevedo, \{Eric Simancas\} and Ocotl{\'a}n Diaz-Parra and Ortiz, \{Jaime Aguilar\} and Trejo-Macotela, \{Francisco Rafael\}",

year = "2025",

doi = "10.13053/CyS-29-2-5664",

language = "English",

volume = "29",

pages = "893--900",

journal = "Computacion y Sistemas",

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publisher = "Instituto Politecnico Nacional",

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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

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 -

Application of GANs for Augmentation of the Mammography Repository

Abstract

UN SDGs

Keywords

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