Ковалев В. А., Козловский С. А., Калиновский А. А. ГЕНЕРАЦИЯ ИСКУССТВЕННЫХ РЕНТГЕНОВСКИХ ИЗОБРАЖЕНИЙ ГРУДНОЙ КЛЕТКИ С ИСПОЛЬЗОВАНИЕМ ГЕНЕРАТИВНО-СОСТЯЗАТЕЛЬНЫХ НЕЙРОННЫХ СЕТЕЙ. Информатика. 2018;15(2):7-16.
1. A survey on deep learning in medical image analysis / G. Litjens [et al.] // Medical Image Analysis. – 2017. – Vol. 42. – P. 60–88.
2. A comparison of conventional and Deep Learning methods of image classification on a database of chest radiographs / V. А. Kovalev [et al.] // Intern. J. of Computer Assisted Radiology and Surgery. – 2017. – Vol. 12, suppl. 1. – P. S139–S140.
3. Benchmarking the efficiency of Deep Learning methods on the problem of predicting subjects’ age by chest radiographs / V. А. Kovalev [et al.] // Proc. 3rd Intern. Conf. Big Data and Advanced Analytics. – Minsk : BSUIR, 2017. – P. 75–82.
4. Generative Adversarial Networks / I. J. Goodfellow [et al.] // arXiv.org [Electronic resource]. – 2014. – Mode of access : https://arxiv.org/pdf/1406.2661. – Date of access : 25.09.2017.
5. Auto-encoding variational bayes / D. P. Kingma [et al.] // arXiv.org [Electronic resource]. – 2014. – Mode of access : https://arxiv.org/pdf/1312.6114. – Date of access : 25.09.2017.
6. LeCun, Y. Gradient-based learning applied to document recognition / Y. LeCun // Proc. of the IEEE. – 1998. – Vol. 86(11). – P. 2278–2324.
7. Unsupervised representation learning with deep convolutional generative adversarial networks / A. Radford, L. Metz, S. Chintala // arXiv.org [Electronic resource]. – 2016. – Mode of access : https://arxiv.org/pdf/1511.06434. – Date of access : 25.09.2017.
8. DCGAN in tensorflow [Electronic resource]. – Mode of access : https://github.com/carpedm20/DCGAN-tensorflow. – Date of access : 25.09.2017.
9. Medical image synthesis with context-aware generative adversarial networks / D. Nie [et al.] // MICCAI, LNCS. –Springer, Cham, 2017. – Vol. 10435. – P. 417–425.
10. Deep MR to CT Synthesis using Unpaired Data / J. M. Wolterink [et al.] // arXiv.org [Electronic resource]. – 2017. – Mode of access : https://arxiv.org/pdf/1708.01155. – Date of access : 22.09.2017.
11. Synthesis of positron emission tomography (PET) images via multi-channel Generative Adversarial Networks (GANs) / L. Bi [et al.] // Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. – Springer, Cham, 2017. – Vol. 10555. – P. 43–51.
