Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images

  • Abder-Rahman AliEmail author
  • Jingpeng Li
  • Thomas Trappenberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11489)


Image segmentation is considered a crucial step in automatic dermoscopic image analysis as it affects the accuracy of subsequent steps. The huge progress in deep learning has recently revolutionized the image recognition and computer vision domains. In this paper, we compare a supervised deep learning based approach with an unsupervised deep learning based approach for the task of skin lesion segmentation in dermoscopy images. Results show that, by using the default parameter settings and network configurations proposed in the original approaches, although the unsupervised approach could detect fine structures of skin lesions in some occasions, the supervised approach shows much higher accuracy in terms of Dice coefficient and Jaccard index compared to the unsupervised approach, resulting in 77.7% vs. 40% and 67.2% vs. 30.4%, respectively. With a proposed modification to the unsupervised approach, the Dice and Jaccard values improved to 54.3% and 44%, respectively.


Deep learning Dermoscopy Melanoma 



The work was supported by Natural Science Foundation of China (Grant No. 71571076).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abder-Rahman Ali
    • 1
    Email author
  • Jingpeng Li
    • 1
  • Thomas Trappenberg
    • 2
  1. 1.Division of Computer Science and MathematicsUniversity of StirlingStirlingUK
  2. 2.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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