Skip to main content
Springer Nature Link
Log in

Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 Workshops (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12535))

Included in the following conference series:

  • 5136 Accesses

Abstract

Skin lesion segmentation is a challenging task due to the large variation of anatomy across different cases. In the last few years, deep learning frameworks have shown high performance in image segmentation. In this paper, we propose Attention Deeplabv3+, an extended version of Deeplabv3+ for skin lesion segmentation by employing the idea of attention mechanism in two stages. We first capture the relationship between the channels of a set of feature maps by assigning a weight for each channel (i.e., channels attention). Channel attention allows the network to emphasize more on the informative and meaningful channels by a context gating mechanism. We also exploit the second level attention strategy to integrate different layers of the atrous convolution. It helps the network to focus on the more relevant field of view to the target. The proposed model is evaluated on three datasets ISIC 2017, ISIC 2018, and \(PH^2\), achieving state-of-the-art performance.

R. Azad and M. Asadi-Aghbolaghi—Contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Source code is available on https://github.com/rezazad68/AttentionDeeplabv3p.

References

  1. who.int/uv/faq/skincancer/en/index1.html

  2. Al-Masni, M.A., Al-antari, M.A., Choi, M.T., Han, S.M., Kim, T.S.: Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput. Methods Programs Biomed. 162, 221–231 (2018)

    Article  Google Scholar 

  3. Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv preprint arXiv:1802.06955 (2018)

  4. Asadi-Aghbolaghi, M., Azad, R., Fathy, M., Escalera, S.: Multi-level context gating of embedded collective knowledge for medical image segmentation. arXiv preprint arXiv:2003.05056 (2020)

  5. Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S.: Bi-directional ConvLSTM U-Net with Densley connected convolutions. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  6. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  7. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected Ds. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  8. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint. arXiv:1706.05587 (2017)

  9. Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp. 801–818 (2018)

    Google Scholar 

  10. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  11. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  12. Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)

  13. Codella, N.C., 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 ISBI 2018, pp. 168–172. IEEE (2018)

    Google Scholar 

  14. Feng, J., Isern, N.G., Burton, S.D., Hu, J.Z.: Studies of secondary melanoma on C57BL/6J mouse liver using 1H NMR metabolomics. Metabolites 3(4), 1011–1035 (2013)

    Article  Google Scholar 

  15. Ghiasi, G., Fowlkes, C.C.: Laplacian pyramid reconstruction and refinement for semantic segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 519–534. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_32

    Chapter  Google Scholar 

  16. Hesamian, M.H., Jia, W., He, X., Kennedy, P.J.: Atrous convolution for binary semantic segmentation of lung nodule. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1015–1019, May 2019

    Google Scholar 

  17. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the CVPR, pp. 7132–7141 (2018)

    Google Scholar 

  18. Li, Y., Shen, L.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2), 556 (2018)

    Article  Google Scholar 

  19. Liang, X., Shen, X., Xiang, D., Feng, J., Lin, L., Yan, S.: Semantic object parsing with local-global long short-term memory. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3185–3193 (2016)

    Google Scholar 

  20. Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1925–1934 (2017)

    Google Scholar 

  21. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  22. Mendonça, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: PH 2-a dermoscopic image database for research and benchmarking. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5437–5440. IEEE (2013)

    Google Scholar 

  23. Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

    Google Scholar 

  24. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the CVPR, pp. 1520–1528 (2015)

    Google Scholar 

  25. Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

  26. Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large kernel matters-improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and pattern recognition, pp. 4353–4361 (2017)

    Google Scholar 

  27. 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). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  28. Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197–207 (2019)

    Article  Google Scholar 

  29. Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2018. CA Cancer J. Clin. 68(1), 7–30 (2018)

    Article  Google Scholar 

  30. Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA: Cancer J. Clin. 69(1), 7–34 (2019)

    Google Scholar 

  31. Sinha, A., Dolz, J.: Multi-scale guided attention for medical image segmentation. arXiv preprint arXiv:1906.02849 (2019)

  32. Tarver, T.: American cancer society. Cancer facts and figures 2014. J. Consum. Health Internet 16, 366–367 (2012)

    Google Scholar 

  33. Wang, G., Luo, P., Lin, L., Wang, X.: Learning object interactions and descriptions for semantic image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5859–5867 (2017)

    Google Scholar 

  34. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)

    Google Scholar 

  35. Zhao, H., et al.: PSANet: point-wise spatial attention network for scene parsing. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 267–283 (2018)

    Google Scholar 

Download references

Acknowledgment

This work has been partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya, and ICREA under the ICREA Academia programme. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.

Author information

Authors and Affiliations

  1. Computer Engineering Department, Sharif University of Technology, Tehran, Iran

    Reza Azad

  2. Computer Science School, Institute for Research in Fundamental Science (IPM), Tehran, Iran

    Maryam Asadi-Aghbolaghi & Mahmood Fathy

  3. Universitat de Barcelona and Computer Vision Center, Barcelona, Spain

    Sergio Escalera

Authors
  1. Reza Azad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maryam Asadi-Aghbolaghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahmood Fathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergio Escalera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryam Asadi-Aghbolaghi .

Editor information

Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S. (2020). Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12535. Springer, Cham. https://doi.org/10.1007/978-3-030-66415-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66415-2_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66414-5

  • Online ISBN: 978-3-030-66415-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics