iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/978-3-031-23236-7_5
Attention Mechanism for Classification of Melanomas | SpringerLink
Skip to main content
Springer Nature Link
Log in

Attention Mechanism for Classification of Melanomas

  • Conference paper
  • First Online:
Optimization, Learning Algorithms and Applications (OL2A 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1754))

Abstract

Melanoma is considered the deadliest type of skin cancer and in the last decade, the incidence rate has increased substantially. However, automatic melanoma classification has been widely used to aid the detection of lesions as well as prevent eventual death. Therefore, in this paper we decided to investigate how an attention mechanism combined with a classical backbone network would affect the classification of melanomas. This mechanism is known as triplet attention, a lightweight method that allows to capture cross-domain interactions. This characteristic helps to acquire rich discriminative feature representations. The different experiments demonstrate the effectiveness of the model in five different datasets. The model was evaluated based on sensitivity, specificity, accuracy, and F1-Score. Even though it is a simple method, this attention mechanism shows that its application could be beneficial in classification tasks.

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
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Shahana sherin, K. C, Shayini, R.: Classification of skin lesions in digital images for the diagnosis of skin cancer. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 162–166. IEEE, India (2020). https://doi.org/10.1109/ICOSEC49089.2020.9215271

  2. 2020 Melanoma Skin Cancer Report: Stemming The Global Epidemic. https://www.melanomauk.org.uk/2020-melanoma-skin-cancer-report. Accessed 20 Jul 2022

  3. Barata, A.C.F.: Automatic detection of melanomas using dermoscopy images. Technical report, Instituto Superior Tecnico Lisboa (2017)

    Google Scholar 

  4. Craythorne, E., Nicholson, P.: Diagnosis and management of skin cancer. Medicine. 51, 2448–2452 (2021). https://doi.org/10.1016/j.mpmed.2021.04.007

  5. Carli, P., et al.: Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. Br. J. Dermatol. (2003). https://doi.org/10.1046/j.1365-2133.2003.05023.x

    Article  Google Scholar 

  6. Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Hosp. J. 6, 94–98 (2019). https://doi.org/10.7861/futurehosp.6-2-94

  7. Chollet, F.: Xception: deep learning with Depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807. IEEE, USA (2017). https://doi.org/10.1109/CVPR.2017.195

  8. Tan, M., Le,: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019). https://doi.org/10.48550/arxiv.1905.11946

  9. Hu, J., Shen, L., Sun, G., Albanie, S.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011–2023. (2020). https://doi.org/10.1109/TPAMI.2019.2913372

  10. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: Convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1

    Chapter  Google Scholar 

  11. Wang, F., et al.: Residual Attention Network for Image Classification (2017). https://doi.org/10.1109/CVPR.2017.683

  12. Boonyuen, K., Kaewprapha, P., Weesakul, U., Srivihok, P.: Convolutional neural network inception-v3: a machine learning approach for leveling short-range rainfall forecast model from satellite image. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2019. LNCS, vol. 11656, pp. 105–115. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26354-6_10

    Chapter  Google Scholar 

  13. Mnih, V., Heess, N., Graves, A., Kavukcuoglu, K.: Recurrent models of visual attention. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2, 2204–2212 (2014)

    Google Scholar 

  14. Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)

    Google Scholar 

  15. Dai, J., et al.: Deformable convolutional network. In: IEEE International Conference on Computer Vision (ICCV), pp. 764–773 (2017). https://doi.org/10.1109/ICCV.2017.89

  16. Park, J., Woo, S., Lee, J.-Y., Kweon, I.: Bam: Bottleneck attention module (2018)

    Google Scholar 

  17. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018). https://doi.org/10.1109/CVPR.2018.00813

  18. Huang, Z., Wang, X., Wei, Y., Huang, L., Shi, H., Liu, W.: CCNet: CRISS-cross attention for semantic segmentation. In: Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1. (2020). https://doi.org/10.1109/TPAMI.2020.3007032

  19. Geng, Z., Guo, M-H., Chen, H., Li, X., Wei, K., Lin, Z.: Is Attention Better Than Matrix Decomposition? (2021)

    Google Scholar 

  20. Liang, S., Gu, Y.: Computer-Aided Diagnosis of Alzheimer’s Disease through Weak Supervision Deep Learning Framework with Attention Mechanism (2020). https://doi.org/10.3390/s21010220

  21. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  22. Jin, Q., Meng, Z., Sun, C., Cui, H., Su, R.: RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans. Front. Bioeng. Biotechnol. (2020). https://doi.org/10.3389/fbioe.2020.605132

  23. 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 

  24. Datta, S.K., Shaikh, M.A., Srihari, S.N., Gao, M.: Soft attention improves skin cancer classification performance. In: Reyes, M., et al. (eds.) IMIMIC/TDA4MedicalData -2021. LNCS, vol. 12929, pp. 13–23. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87444-5_2

    Chapter  Google Scholar 

  25. Yan, Y., Kawahara, J., Hamarneh, G.: Melanoma recognition via visual attention. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 793–804. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_62

    Chapter  Google Scholar 

  26. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2014). arXiv:1409.1556

  27. Misra, D., Nalamada, T., Arasanipalai, A., Hou, Q.: Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 3138–3147 (2020). https://doi.org/10.1109/WACV48630.2021.00318

  28. Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci. Data 8, 34 (2021). https://doi.org/10.1038/s41597-021-00815-z

    Article  Google Scholar 

  29. Tschandl P., Rosendahl C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions (2018). https://doi.org/10.1038/sdata.2018.161

  30. Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI). In: International Skin Imaging Collaboration (ISIC) (2017). https://doi.org/10.1109/ISBI.2018.8363547

  31. Combalia, M., et al.: BCN20000: Dermoscopic Lesions in the Wild (2019). arXiv:1908.02288

  32. Mendonça, T., Ferreira, P.M., Marques, J., Marcal, A.R.S., Rozeira, J.: PH\(^2\) - A dermoscopic image database for research and benchmarking. In: 35th International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan (2013)

    Google Scholar 

  33. DermNet NZ. All about the skin. https://dermnetnz.org/image-library. Accessed 26 Jun 2022

  34. Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. In: IEEE Journal of Biomedical Health Informatics (IEEE JBHI) special issue on Skin Lesion Image Analysis for Melanoma Detection (2019). https://doi.org/10.1109/JBHI.2018.2824327

  35. Kingma, D., Ba, Jimmy.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015) (2015)

    Google Scholar 

  36. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520. IEEE, USA (2018). https://doi.org/10.1109/CVPR.2018.00474

  37. Indraswari, R., Rokhana, R., Herulambang, W.: Melanoma image classification based on MobileNetV2 network. Proc. Comput. Sci. 197, 198–207 (2022). https://doi.org/10.1016/j.procs.2021.12.132

Download references

Author information

Authors and Affiliations

  1. School of Science and Technology, University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal

    Cátia Loureiro, Vítor Filipe & Lio Gonçalves

  2. INESC Technology and Science (INESC TEC), Porto, Portugal

    Vítor Filipe & Lio Gonçalves

Authors
  1. Cátia Loureiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vítor Filipe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lio Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cátia Loureiro .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Loureiro, C., Filipe, V., Gonçalves, L. (2022). Attention Mechanism for Classification of Melanomas. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23236-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23235-0

  • Online ISBN: 978-3-031-23236-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation