Publicación:
High-resolution generative adversarial neural networks applied to histological images generation

Página de artículo simple
dc.contributor.author Mauricio A. es_PE
dc.contributor.author López J. es_PE
dc.contributor.author Huauya R. es_PE
dc.contributor.author Diaz J. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2018
dc.description The present work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU), the Office of Research of Universidad Nacional de Ingeniería (VRI - UNI) and the research management office (OGI - UNI).
dc.description.abstract For many years, synthesizing photo-realistic images has been a highly relevant task due to its multiple applications from aesthetic or artistic [19] to medical purposes [1, 6, 21]. Related to the medical area, this application has had greater impact because most classification or diagnostic algorithms require a significant amount of highly specialized images for their training yet obtaining them is not easy at all. To solve this problem, many works analyze and interpret images of a specific topic in order to obtain a statistical correlation between the variables that define it. By this way, any set of variables close to the map generated in the previous analysis represents a similar image. Deep learning based methods have allowed the automatic extraction of feature maps which has helped in the design of more robust models photo-realistic image synthesis. This work focuses on obtaining the best feature maps for automatic generation of synthetic histological images. To do so, we propose a Generative Adversarial Networks (GANs) [8] to generate the new sample distribution using the feature maps obtained by an autoencoder [14, 20] as latent space instead of a completely random one. To corroborate our results, we present the generated images against the real ones and their respective results using different types of autoencoder to obtain the feature maps.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1007/978-3-030-01421-6_20
dc.identifier.isbn 9783030014209
dc.identifier.scopus 2-s2.0-85054798854
dc.identifier.uri https://hdl.handle.net/20.500.12390/634
dc.language.iso eng
dc.publisher Springer Verlag
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Statistical correlation
dc.subject Deep learning es_PE
dc.subject Diagnosis es_PE
dc.subject Medical imaging es_PE
dc.subject Neural networks es_PE
dc.subject Diagnostic algorithms es_PE
dc.subject Generative Adversarial Nets es_PE
dc.subject High resolution es_PE
dc.subject Histological images es_PE
dc.subject Learning-based methods es_PE
dc.subject Photo realistic image synthesis es_PE
dc.subject Photorealistic images es_PE
dc.subject Image analysis es_PE
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#2.02.04
dc.title High-resolution generative adversarial neural networks applied to histological images generation
dc.type info:eu-repo/semantics/conferenceObject
dspace.entity.type Publication
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
Archivos
Colecciones
1.1 Eventos institucionales
 2.2 Estudios de maestría