Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data

Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data

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The authors propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but the amount of labelled data is limited. They consider the specific case of classifying skin lesions as either benign or malignant. In this setting, the authors’ proposed approach – the semi-supervised, denoising adversarial autoencoder – is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. They perform an ablation study to analyse the contributions of both the adversarial and denoising components and compare their work with state-of-the-art results. They find that their model yields superior classification performance, especially when evaluating their model at high sensitivity values.


    1. 1)
      • 1. Esteva, A., Kuprel, B., Novoa, R.A., et al: ‘Dermatologist-level classification of skin cancer with deep neural networks’, Nature, 2017, 542, (7639), pp. 115118.
    2. 2)
      • 2. Bengio, Y., Yao, L., Alain, G., et al: ‘Generalized denoising auto-encoders as generative models’. Advances in Neural Information Processing Systems, 2013, pp. 899907.
    3. 3)
      • 3. Im, D.J., Ahn, S., Memisevic, R., et al: ‘Denoising criterion for variational auto-encoding framework’. arXiv preprint arXiv:1511.06406, 2015.
    4. 4)
      • 4. Kingma, D.P., Welling, M.: ‘Auto-encoding variational Bayes’. Proc. 2015 Int. Conf. Learning Representations (ICLR-2015), arXiv preprint arXiv:1312.6114, 2014.
    5. 5)
      • 5. Makhzani, A., Shlens, J., Jaitly, N., et al: ‘Adversarial autoencoders’. arXiv preprint arXiv:1511.05644, 2015.
    6. 6)
      • 6. Vincent, P., Larochelle, H., Bengio, Y., et al: ‘Extracting and composing robust features with denoising autoencoders’. Proc. 25th Int. Conf. Machine Learning, 2008, pp. 10961103.
    7. 7)
      • 7. Vincent, P., Larochelle, H., Lajoie, I., et al: ‘Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion’, J. Mach. Learn. Res., 2010, 11, (Dec), pp. 33713408.
    8. 8)
      • 8. International skin imaging collaboration: Melanoma project..
    9. 9)
      • 9. Codella, N.C.F., Gutman, D., Celebi, M.E., 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)’. CoRR, abs/1710.05006, 2017.
    10. 10)
      • 10. Menegola, A., Tavares, J., Fornaciali, M., et al: ‘RECOD titans at ISIC challenge 2017’. CoRR, abs/1703.04819, 2017.
    11. 11)
      • 11. González-Daz, I.: ‘Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions’. CoRR, abs/1703.01976, 2017.
    12. 12)
      • 12. Matsunaga, K., Hamada, A., Minagawa, A., et al: ‘Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble’. CoRR, abs/1703.03108, 2017.
    13. 13)
      • 13. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al: ‘Generative adversarial nets’. Advances in Neural Information Processing Systems, 2014, pp. 26722680.
    14. 14)
      • 14. Radford, A., Metz, L., Chintala, S.: ‘Unsupervised representation learning with deep convolutional generative adversarial networks’. Int. Conf. Learning Representations (ICLR, 2016), arXiv preprint arXiv:1511.06434, 2015.

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