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2018 | OriginalPaper | Buchkapitel

High-Resolution Generative Adversarial Neural Networks Applied to Histological Images Generation

verfasst von: Antoni Mauricio, Jorge López, Roger Huauya, Jose Diaz

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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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.
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Metadaten
Titel
High-Resolution Generative Adversarial Neural Networks Applied to Histological Images Generation
verfasst von
Antoni Mauricio
Jorge López
Roger Huauya
Jose Diaz
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-01421-6_20

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