Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly. Currently, Deep Neural Networks are the state-of-the-art results on automated skin cancer classification. To push the results further, we need to address the lack of annotated data, which is expensive and require much effort from specialists. To bypass this problem, we propose using Generative Adversarial Networks for generating realistic synthetic skin lesion images. To the best of our knowledge, our results are the first to show visually-appealing synthetic images that comprise clinically-meaningful information.


Skin cancer Generative models Deep learning 



We gratefully acknowledge NVIDIA for the donation of GPUs, Microsoft Azure for the GPU-powered cloud platform, and CCES/Unicamp (Center for Computational Engineering & Sciences) for the GPUs used in this work. A. Bissoto is funded by CNPq. E. Valle is partially funded by Google Research LATAM 2017, CNPq PQ-2 grant (311905/2017-0), and Universal grant (424958/2016-3). RECOD Lab. is partially supported by FAPESP, CNPq, and CAPES.


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. Technical report No. EPFL-REPORT-149300 (2010)Google Scholar
  2. 2.
    American Cancer Society: Survival rates for melanoma skin cancer, by stage (2016).
  3. 3.
    Argenziano, G., et al.: Dermoscopy: a tutorial. EDRA, Medical Publishing & New Media, p. 16 (2002)Google Scholar
  4. 4.
    Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J.: A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Celebi, M., Schaefer, G. (eds.) Color Medical Image Analysis. LNCVB, vol. 6, pp. 63–86. Springer, Dordrecht (2013). Scholar
  5. 5.
    Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC). arXiv:1710.05006 (2017)
  6. 6.
    Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)CrossRefGoogle Scholar
  7. 7.
    Fornaciali, M., Carvalho, M., Bittencourt, F.V., Avila, S., Valle, E.: Towards automated melanoma screening: proper computer vision & reliable results. arXiv:1604.04024 (2016)
  8. 8.
    Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)Google Scholar
  9. 9.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE CVPR (2016)Google Scholar
  10. 10.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: IEEE CVPR (2017)Google Scholar
  11. 11.
    Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: ICLR (2018)Google Scholar
  12. 12.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). Scholar
  13. 13.
    Mendonça, T., Ferreira, P., Marques, J., Marcal, A., Rozeira, J.: PH\(^2\): a dermoscopic image database for research and benchmarking. In: IEEE EMBS (2013)Google Scholar
  14. 14.
    Menegola, A., Tavares, J., Fornaciali, M., Li, L.T., Avila, S., Valle, E.: RECOD titans at ISIC challenge 2017. arXiv:1703.04819 (2017)
  15. 15.
    Perez, F., Vasconcelos, C., Avila, S., Valle, E.: Data augmentation for skin lesion analysis. In: ISIC Skin Image Analysis Workshop (2018)Google Scholar
  16. 16.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)Google Scholar
  17. 17.
    Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: NIPS (2016)Google Scholar
  18. 18.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI (2017)Google Scholar
  19. 19.
    Valle, E., et al.: Data, depth, and design: learning reliable models for melanoma screening. arXiv:1711.00441 (2018)
  20. 20.
    Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: IEEE CVPR (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alceu Bissoto
    • 1
  • Fábio Perez
    • 2
  • Eduardo Valle
    • 2
  • Sandra Avila
    • 1
    Email author
  1. 1.RECOD Lab, ICUniversity of Campinas (Unicamp)CampinasBrazil
  2. 2.RECOD Lab, DCA, FEECUniversity of Campinas (Unicamp)CampinasBrazil

Personalised recommendations