Novel Ensembling Methods for Dermatological Image Classification

  • Tamás NyíriEmail author
  • Attila Kiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)


In this paper we investigate multiple novel techniques of ensembling deep neural networks with different hyperparameters and differently preprocessed data for skin lesion classification. To this end, we have utilized the datasets made public by two of the most recent “Skin Lesion Analysis Towards Melanoma Detection” grand challenges (ISIC2017 and ISIC2018). The datasets provided by these two challenges differ in multiple aspects: the size, quality and origin of the images, the number of possible target lesion categories and the metrics used for ranking. We will show that ensembling can be surprisingly useful not only for combining different machine learning models but also for combining different hyperparameter choices of these models and multiple strategies for preprocessing the input data at the task of skin lesion detection, outperforming more mainstream methods like hyperparameter optimization and test-time augmentation both in terms of speed and accuracy.


Neural networks Medical image analysis Ensemble learning 



The project has been supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002).

We would like to express our special appreciation and thanks to Miklós Sárdy MD, PhD, associate professor (Head of the Department of Dermatology, Venereology and Dermatooncology at Semmelweis University, Faculty of Medicine) for his advice on the diagnosis of skin lesions and to Attila Ulbert PhD for providing part of the hardware infrastructure and his help with the editing of the document.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5. Accessed 04 Aug 2018
  6. 6.
  7. 7.
  8. 8. Accessed 04 Aug 2018
  9. 9. Accessed 04 Aug 2018
  10. 10. Accessed 04 Aug 2018
  11. 11.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807 (2017)Google Scholar
  12. 12.
    Codella, N.C.F., 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). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172 (2018)Google Scholar
  13. 13.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)Google Scholar
  14. 14.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  15. 15.
    Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)Google Scholar
  16. 16.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)Google Scholar
  17. 17.
    Matsunaga, K., Hamada, A., Minagawa, A., Koga, H.: Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble. CoRR abs/1703.03108 (2017)Google Scholar
  18. 18.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  19. 19.
    Shi, Z., He, L., Suzuki, K., Nakamura, T., Itoh, H.: Survey on neural networks used for medical image processing. Int. J. Comput. Sci. 3(1), 86–100 (2009)Google Scholar
  20. 20.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)Google Scholar
  21. 21.
    Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI (2017)Google Scholar
  22. 22.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)Google Scholar
  23. 23.
    Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. In: Scientific data (2018)CrossRefGoogle Scholar
  24. 24.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: NIPS (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Information SystemsELTE Eötvös Loránd UniversityBudapestHungary

Personalised recommendations