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SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks

  • Md. Mostafa Kamal SarkerEmail author
  • Hatem A. Rashwan
  • Farhan Akram
  • Syeda Furruka Banu
  • Adel Saleh
  • Vivek Kumar Singh
  • Forhad U. H. Chowdhury
  • Saddam Abdulwahab
  • Santiago Romani
  • Petia Radeva
  • Domenec Puig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we formulated a new loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the boundaries of melanoma regions. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of the segmentation accuracy. Moreover, it is capable of segmenting about 100 images of a \(384\times 384\) size per second on a recent GPU.

Keywords

Skin lesion segmentation melanoma Deep learning Dilated residual networks Pyramid pooling 

Notes

Acknowledgement

This research is funded by the program Marti Franques under the agreement between Universitat Rovira i Virgili and Fundació Catalunya La Pedrera.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Md. Mostafa Kamal Sarker
    • 1
    Email author
  • Hatem A. Rashwan
    • 1
  • Farhan Akram
    • 2
  • Syeda Furruka Banu
    • 3
  • Adel Saleh
    • 1
  • Vivek Kumar Singh
    • 1
  • Forhad U. H. Chowdhury
    • 4
  • Saddam Abdulwahab
    • 1
  • Santiago Romani
    • 1
  • Petia Radeva
    • 5
  • Domenec Puig
    • 1
  1. 1.DEIMUniversitat Rovira i VirgiliTarragonaSpain
  2. 2.Imaging Informatics DivisionBioinformatics InstituteSingaporeSingapore
  3. 3.ETSEQUniversitat Rovira i VirgiliTarragonaSpain
  4. 4.Liverpool School of Tropical MedicineLiverpoolUK
  5. 5.Department of MathematicsUniversity of BarcelonaBarcelonaSpain

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