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2019 | OriginalPaper | Chapter

Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning

Authors : Simone Bonechi, Monica Bianchini, Pietro Bongini, Giorgio Ciano, Giorgia Giacomini, Riccardo Rosai, Linda Tognetti, Alberto Rossi, Paolo Andreini

Published in: New Trends in Image Analysis and Processing – ICIAP 2019

Publisher: Springer International Publishing

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Abstract

Early diagnosis of skin lesions is essential for the positive outcome of the disease, which can only be resolved with surgical treatment. In this manuscript, a deep learning method is proposed for the classification of cutaneous lesions based on their visual appearance and on the patient’s anamnestic data. These include age and gender of the patient and position of the lesion. The classifier discriminates between benign and malignant lesions, mimicking a typical procedure in dermatological diagnostics. Good preliminary results on the ISIC Dataset demonstrate the importance of the information fusion process, which significantly improves the classification accuracy.

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https://isic-archive.com/.

He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

Chen, L.C., et al.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)

Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRef

Andreini, P., Bonechi, S., Bianchini, M., Mecocci, A., Scarselli, F.: A deep learning approach to bacterial colony segmentation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 522–533. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_51CrossRef

Rossi, A., et al.: Analysis of brain NMR images for age estimation with deep learning

Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)CrossRef

Yap, J., Yolland, W., Tschandl, P.: Multimodal skin lesion classification using deep learning. Exp. Dermatol. 27(11), 1261–1267 (2018)CrossRef

Leiter, U., Eigentler, T., Garbe, C.: Epidemiology of skin cancer. Adv. Exp. Med. Biol. 810, 120–140 (2014)

Apalla, Z., et al.: Skin cancer: epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approaches. Dermatol. Ther. 7(1), 5–19 (2017)CrossRef

10.

Paolino, G., et al.: Histology of non-melanoma skin cancers: an update. Biomedicines 5(4), 71 (2017)CrossRef

11.

Apalla, Z., et al.: Epidemiological trends in skin cancer. Dermatol. Pract. Conceptual 7(2), 1 (2017)

12.

Rastrelli, M., et al.: Melanoma: epidemiology, risk factors, pathogenesis, diagnosis and classification. Vivo 28(6), 1005–1011 (2014)

13.

Schadendorf, D., Hauschild, A.: Melanoma in 2013: melanoma—the run of success continues. Nat. Rev. Clin. Oncol. 11(2014), 75–76 (2013)

14.

Globocan. https://gco.iarc.fr/. Accessed 06 June 2019

15.

Matthews, N.H., et al.: Epidemiology of melanoma (2017)

16.

Domingues, B., et al.: Melanoma treatment in review. ImmunoTargets Ther. 7, 35 (2018)CrossRef

17.

Gandini, S., et al.: Meta-analysis of risk factors for cutaneous melanoma: II. Sun exposure. Eur. J. Cancer 41(1), 45–60 (2005)CrossRef

18.

Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by ISIC. In: 15th International Symposium on Biomedical Imaging, pp. 168–172. IEEE (2018)

19.

Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRef

20.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

21.

Tognetti, L., et al.: An integrated clinical-dermoscopic risk scoring system for the differentiation between early melanoma and atypical nevi: the iDScore. J. Eur. Acad. Dermatol. Venereology 32(12), 2162–2170 (2018)CrossRef

Title: Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning
Authors: Simone Bonechi
Monica Bianchini
Pietro Bongini
Giorgio Ciano
Giorgia Giacomini
Riccardo Rosai
Linda Tognetti
Alberto Rossi
Paolo Andreini
Publisher: Springer International Publishing
Book: New Trends in Image Analysis and Processing – ICIAP 2019
Print ISBN: 978-3-030-30753-0

Electronic ISBN: 978-3-030-30754-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30754-7_21

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