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

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dc.contributor.authorMauricio, Antoni
dc.contributor.authorLópez, Jorge
dc.contributor.authorHuauya, Roger
dc.contributor.authorDiaz Rosado, Jose Carlos
dc.date.accessioned2019-01-29T22:19:49Z
dc.date.available2019-01-29T22:19:49Z
dc.date.issued2018
dc.description.abstractFor 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. © Springer Nature Switzerland AG 2018.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.identifier.doihttps://doi.org/10.1007/978-3-030-01421-6_20es_PE
dc.identifier.isbnurn:isbn:9783030014209es_PE
dc.identifier.issn3029743es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15767
dc.language.isoenges_PE
dc.publisherSpringer Verlages_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85054798854&doi=10.1007%2f978-3-030-01421-6_20&partnerID=40&md5=e55a2f7ce4ae4e89c576a276ec1cc424es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectDeep learninges_PE
dc.subjectDiagnosises_PE
dc.subjectMedical imaginges_PE
dc.subjectNeural networkses_PE
dc.subjectDiagnostic algorithmses_PE
dc.subjectGenerative Adversarial Netses_PE
dc.subjectHigh resolutiones_PE
dc.subjectHistological imageses_PE
dc.subjectLearning-based methodses_PE
dc.subjectPhoto realistic image synthesises_PE
dc.subjectPhotorealistic imageses_PE
dc.subjectStatistical correlationes_PE
dc.subjectImage analysises_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleHigh-resolution generative adversarial neural networks applied to histological images generationes_PE
dc.typeinfo:eu-repo/semantics/article
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