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Model Regularisation for Skin Lesion Symmetry Classification: SymDerm v2.0

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Computer Analysis of Images and Patterns (CAIP 2023)

Abstract

Symmetry is one of the distinguishing features when diagnosing the malignancy of skin lesions. In this work, we introduce an extension of the SymDerm dataset with around 2000 new annotations, and analyze 1) the effect of different data augmentation techniques on learning the skin lesion symmetry classification task, and 2) how the learning of this task is affected when combined with the classification of its malignancy in a multitask learning environment. We conclude that, although not all data augmentation techniques improve classification performance, these techniques achieve an increase of approximately 7.7% for B.Acc and Precision, 8.0% for Recall and F1-score, and 15.08% for the Kappa score. Moreover, we show that symmetry classification benefits from the introduction of an auxiliary task by stabilizing the learning curve and decreasing the train-validation learning gap.

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Notes

  1. 1.

    www.isic-archive.com.

  2. 2.

    www.dermis.net.

  3. 3.

    Was deactivated on December 31, 2019.

References

  1. Argenziano, G., Soyer, H., De Giorgi, V., Piccolo, D., Carli, P., Delfino, M.: Interactive atlas of dermoscopy. EDRA Medical Publishing & New media (2000)

    Google Scholar 

  2. Argenziano, G., et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the internet. J. Am. Acad. Dermatol. 48(5), 679–693 (2003)

    Article  Google Scholar 

  3. Campos-do Carmo, G., Ramos-e Silva, M.: Dermoscopy: basic concepts. Int. J. Dermatol. 47(7), 712–719 (2008)

    Article  Google Scholar 

  4. Ferrara, G., et al.: Dermoscopic and histopathologic diagnosis of equivocal melanocytic skin lesions: an interdisciplinary study on 107 cases. Cancer 95(5), 1094–1100 (2002)

    Article  Google Scholar 

  5. Finlayson, G.D., Trezzi, E.: Shades of gray and colour constancy. In: Color and Imaging Conference, vol. 2004, pp. 37–41. Society for Imaging Science and Technology (2004)

    Google Scholar 

  6. Mendonça, T., et al.: \({PH}^2\): a public database for the analysis of dermoscopic images. Dermoscopy Image Anal. (2015)

    Google Scholar 

  7. Premaladha, J., Ravichandran, K.: Asymmetry analysis of malignant melanoma using image processing: a survey. J. Artif. Intell. 7(2), 45 (2014)

    Article  Google Scholar 

  8. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  9. Talavera-Martínez, L., et al.: A novel approach for skin lesion symmetry classification with a deep learning model. Comput. Biol. Med. 145, 105450 (2022)

    Article  Google Scholar 

  10. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1–9 (2018)

    Article  Google Scholar 

  11. Tschandl, P., et al.: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 20(7), 938–947 (2019)

    Article  Google Scholar 

  12. Wang, H., et al.: Score-cam: score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 24–25 (2020)

    Google Scholar 

Download references

Acknowledgements

This paper is part of the R+D+i Project PID2020-113870GB-I00 - “Desarrollo de herramientas de Soft Computing para la Ayuda al Diagnóstico Clínico y a la Gestión de Emergencias (HESOCODICE)”, funded by MCIN/AEI/10.13039/501100011033/.

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Correspondence to Lidia Talavera-Martínez .

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Talavera-Martínez, L., Bibiloni, P., Giacaman, A., Taberner, R., Del Pozo Hernando, L.J., González-Hidalgo, M. (2023). Model Regularisation for Skin Lesion Symmetry Classification: SymDerm v2.0. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14184. Springer, Cham. https://doi.org/10.1007/978-3-031-44237-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-44237-7_10

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  • Online ISBN: 978-3-031-44237-7

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