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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

## 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.
Literatur
1.
Asperti, A., Mastronardo, C.: The effectiveness of data augmentation for detection of gastrointestinal diseases from endoscopical images. arXiv preprint arXiv:​1712.​03689 (2017)
2.
Bengio, Y.: Learning deep architectures for AI. Found. Trends $$\textregistered$$ Mach. Learn. 2(1), 1–127 (2009) CrossRef
3.
Calimeri, F., Marzullo, A., Stamile, C., Terracina, G.: Biomedical data augmentation using generative adversarial neural networks. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10614, pp. 626–634. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-68612-7_​71 CrossRef
4.
Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 215–223 (2011)
5.
Doersch, C.: Tutorial on variational autoencoders. arXiv preprint arXiv:​1606.​05908 (2016)
6.
Eaton-Rosen, Z., Bragman, F., Ourselin, S., Cardoso, M.J.: Improving data augmentation for medical image segmentation (2018)
7.
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)
8.
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
9.
Hitawala, S.: Comparative study on generative adversarial networks. arXiv preprint arXiv:​1801.​04271 (2018)
10.
Hou, L., et al.: Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. arXiv preprint arXiv:​1704.​00406 (2017)
11.
Kastaniotis, D., Ntinou, I., Tsourounis, D., Economou, G., Fotopoulos, S.: Attention-aware generative adversarial networks (ATA-GANs). arXiv preprint arXiv:​1802.​09070 (2018)
12.
Komura, D., Ishikawa, S.: Machine learning methods for histopathological image analysis. Comput. Struct. Biotechnol. J. 16, 34–42 (2018) CrossRef
13.
Kumar, A., Sattigeri, P., Fletcher, T.: Semi-supervised learning with GANs: manifold invariance with improved inference. In: Advances in Neural Information Processing Systems, pp. 5534–5544 (2017)
14.
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian pyramid networks for fast and accurate superresolution. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, p. 5 (2017)
15.
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. arXiv preprint arXiv:​1511.​05644 (2015)
16.
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:​1802.​05957 (2018)
17.
Song, J., Zhao, S., Ermon, S.: A-NICE-MC: adversarial training for MCMC. In: Advances in Neural Information Processing Systems, pp. 5140–5150 (2017)
18.
Tom, F., Sheet, D.: Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1174–1177. IEEE (2018)
19.
Van Den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with PixelCNN decoders. In: Advances in Neural Information Processing Systems, pp. 4790–4798 (2016)
20.
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
21.
Zhang, H., Xie, X., Fang, C., Yang, Y., Jin, D., Fei, P.: High-throughput, high-resolution generated adversarial network microscopy. arXiv preprint arXiv:​1801.​07330 (2018)