Evolution of conditional-GANs for the synthesis of chest X-ray images


Por: Rodriguez-de-la-Cruz, Juan-Antonio, Acosta-Mesa, Hector-Gabriel, Mezura-Montes, Efren, Ardmbula Cosio, Fernando, Escalante-Ramirez, Boris, Olveres Montiel, Jimena

Publicada: 1 ene 2021
Resumen:
Deep learning (DL) is now widely used to perform tasks involving the analysis of biomedical imaging. However, the small amounts available of annotated examples of these types of images make it difficult to use DL-based systems, since large amounts of data are required for adequate generalization and performance. For this reason, in recent years, Generative Adversarial Networks (GANs) have been used to obtain synthetic images that artificially increase the amount available. Despite this, the usual training instability in GANs, in addition to their empirical design, does not always allow for high-quality results. Through the neuroevolution of GANs it has been possible to reduce these problems, but many of these works use benchmark datasets with thousands of images, a scenario that does not reflect the real conditions of cases in which it is necessary to increase the data due to the limited amount available. In this work is presented cDCGAN-PSO, an algorithm for the neuroevolution of conditional-GANs (cGAN) that adapts the concepts of a previously reported neuroevolutionary algorithm called DCGAN-PSO, which was focused on the design and training of DCGANs through the use of Particle Swarm Optimization, a Swarm Intelligence algorithm that uses a set of potential solutions to approximate a highly competitive solution. The evolved cGANs allows the synthesis of three classes of chest X-ray images and they were trained with only 600 images of each class. The synthetic images obtained of each class show good similarity with real chest X-ray images.

Filiaciones:
Rodriguez-de-la-Cruz, Juan-Antonio:
 Univ Veracruz, Inst Invest Inteligencia Artificial, Campus Sur,Paseo 112, Xalapa 91097, Veracruz, Mexico

Acosta-Mesa, Hector-Gabriel:
 Univ Veracruz, Inst Invest Inteligencia Artificial, Campus Sur,Paseo 112, Xalapa 91097, Veracruz, Mexico

Mezura-Montes, Efren:
 Univ Veracruz, Inst Invest Inteligencia Artificial, Campus Sur,Paseo 112, Xalapa 91097, Veracruz, Mexico

Ardmbula Cosio, Fernando:
 Univ Nacl Autonoma Mexico, Inst Invest Matemdt Aplicadas & Sistemas, Campus Sur,Paseo 112, Xalapa 91097, Veracruz, Mexico

Escalante-Ramirez, Boris:
 Univ Nacl Autonoma Mexico, Fac Ingn, Univ 3000,Ciudad Univ, Cd Mx 04510, Mexico

Olveres Montiel, Jimena:
 Univ Nacl Autonoma Mexico, Fac Ingn, Univ 3000,Ciudad Univ, Cd Mx 04510, Mexico
ISSN: 0277786X





OPTICS, PHOTONICS, AND DIGITAL TECHNOLOGIES FOR IMAGING APPLICATIONS VIII
Editorial
SPIE-INT SOC OPTICAL ENGINEERING, 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA, Estados Unidos America
Tipo de documento: Proceedings Paper
Volumen: 12088 Número:
Páginas:
WOS Id: 000797319900010

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