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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15767
Title: High-resolution generative adversarial neural networks applied to histological images generation
Authors: Mauricio, Antoni
López, Jorge
Huauya, Roger
Diaz Rosado, Jose Carlos
Keywords: Deep learning;Diagnosis;Medical imaging;Neural networks;Diagnostic algorithms;Generative Adversarial Nets;High resolution;Histological images;Learning-based methods;Photo realistic image synthesis;Photorealistic images;Statistical correlation;Image analysis
Issue Date: 2018
Publisher: Springer Verlag
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054798854&doi=10.1007%2f978-3-030-01421-6_20&partnerID=40&md5=e55a2f7ce4ae4e89c576a276ec1cc424
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. © Springer Nature Switzerland AG 2018.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15767
ISBN: 9783030014209
ISSN: 3029743
Appears in Collections:Artículos de investigación

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