Figure 1. Structure of Pretrained Discriminator
Figure 2. Structure of Cycle GAN Network
Figure 3. t-SNE Unpaired Test Data (700 normal samples, 700 cancer samples)
Figure 4. t-SNE Paired Test Data (600 normal samples, 600 cancer samples)
Table 1. Data Description
Table 2. Data Description
Table 3. Unpaired Test Data (700 normal samples, 700 cancer samples)
Table 4. Paired Test Data (600 normal samples, 600 cancer samples)
Supported by : Handong Global University
- Y. LeCun, Y. Bengio, and G. J. n. Hinton, "Deep learning," vol. 521, no. 7553, p. 436, 2015. https://doi.org/10.1038/nature14539
- D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. J. a. p. a. Beck, "Deep learning for identifying metastatic breast cancer," 2016.
- I. Goodfellow et al., "Generative adversarial nets," in Advances in neural information processing systems, 2014, pp. 2672-2680.
- J.-Y. Zhu, T. Park, P. Isola, and A. A. J. a. p. Efros, "Unpaired image-to-image translation using cycle-consistent adversarial networks," 2017.
- K. Tomczak, P. Czerwinska, and M. J. C. o. Wiznerowicz, "The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge," vol. 19, no. 1A, p. A68, 2015.
- J. N. Weinstein et al., "The cancer genome atlas pan-cancer analysis project," vol. 45, no. 10, p. 1113, 2013. https://doi.org/10.1038/ng.2764
- G. C. J. Science, "The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans," vol. 348, no. 6235, pp. 648-660, 2015. https://doi.org/10.1126/science.1262110