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

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11140)


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.


  • Generative Adversarial Nets
  • Histological images
  • High-resolution generated images

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).

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  • DOI: 10.1007/978-3-030-01421-6_20
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Correspondence to Antoni Mauricio or Jose Diaz .

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Mauricio, A., López, J., Huauya, R., Diaz, J. (2018). High-Resolution Generative Adversarial Neural Networks Applied to Histological Images Generation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham.

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