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schemes for automated diagnosis of lentigo on confocal microscopy images. In:
International Conference on Signal and Image Processing (ICSIP), pp. 143–147,
July 2019
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Eve, O.: Les produits d´ epigmentants: le point en 2011, p. 78, September 2011
14. Cohen, L.M.: Lentigo maligna and lentigo maligna melanoma. J. Am. Acad. Dermatol. 33(6), 923–936 (1995)
15. Bollea-Garlatti, L.A., Galimberti, G.N., Galimberti, R.L.: Lentigo maligna: keys
to dermoscopic diagnosis. Actas Dermo-Sifiliogr´ aficas (English Edition) 107(6),
489–497 (2016)
16. Andersen, W.K., Labadie, R.R., Bhawan, J.: Histopathology of solar lentigines of
the face: a quantitative study. J. Am. Acad. Dermatol. 36(3), 444–447 (1997)
17. Chaari, L.: A Bayesian grouplet transform. SIViP 13(5), 871–878 (2019). https://
doi.org/10.1007/s11760-019-01423-6
18. Ribani, R., Marengoni, M.: A survey of transfer learning for convolutional neural
networks. In: Conference on Graphics, Patterns and Images Tutorials, pp. 47–57
(2019)
19. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep
learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-01970
20. Garc´ ıa, A.H., K¨ onig, P.: Further advantages of data augmentation on convolutional neural networks. In: K˚ urkov´ a, V., Manolopoulos, Y., Hammer, B., Iliadis,
L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 95–103. Springer,
Cham (2018). https://doi.org/10.1007/978-3-030-01418-6 10
21. Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015
22. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in
deep neural networks? Computing Research Repository (CoRR), pp. 3320–3328
(2014)
Open Access This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/),
which permits use, sharing, adaptation, distribution and reproduction in any medium
or format, as long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license and indicate if changes were
made.
The images or other third party material in this chapter are included in the
chapter’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the chapter’s Creative Commons license and
your intended use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright holder.
99
12. Cendre, R., Mansouri, A., Benezeth, Y., Marzani, F., Jean, P., Cinotti, E.: Two
schemes for automated diagnosis of lentigo on confocal microscopy images. In:
International Conference on Signal and Image Processing (ICSIP), pp. 143–147,
July 2019
13. `
Eve, O.: Les produits d´ epigmentants: le point en 2011, p. 78, September 2011
14. Cohen, L.M.: Lentigo maligna and lentigo maligna melanoma. J. Am. Acad. Dermatol. 33(6), 923–936 (1995)
15. Bollea-Garlatti, L.A., Galimberti, G.N., Galimberti, R.L.: Lentigo maligna: keys
to dermoscopic diagnosis. Actas Dermo-Sifiliogr´ aficas (English Edition) 107(6),
489–497 (2016)
16. Andersen, W.K., Labadie, R.R., Bhawan, J.: Histopathology of solar lentigines of
the face: a quantitative study. J. Am. Acad. Dermatol. 36(3), 444–447 (1997)
17. Chaari, L.: A Bayesian grouplet transform. SIViP 13(5), 871–878 (2019). https://
doi.org/10.1007/s11760-019-01423-6
18. Ribani, R., Marengoni, M.: A survey of transfer learning for convolutional neural
networks. In: Conference on Graphics, Patterns and Images Tutorials, pp. 47–57
(2019)
19. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep
learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-01970
20. Garc´ ıa, A.H., K¨ onig, P.: Further advantages of data augmentation on convolutional neural networks. In: K˚ urkov´ a, V., Manolopoulos, Y., Hammer, B., Iliadis,
L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 95–103. Springer,
Cham (2018). https://doi.org/10.1007/978-3-030-01418-6 10
21. Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015
22. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in
deep neural networks? Computing Research Repository (CoRR), pp. 3320–3328
(2014)
Open Access This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/),
which permits use, sharing, adaptation, distribution and reproduction in any medium
or format, as long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license and indicate if changes were
made.
The images or other third party material in this chapter are included in the
chapter’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the chapter’s Creative Commons license and
your intended use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright holder.