Subject: Computer Science - Computer Vision and Pattern Recognition
This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma Detection" (ISBI 2017). Although our team has a long experience with melanoma classification, the ISIC Challenge 2017 wa... View more
 M. Fornaciali, S. Avila, M. Carvalho, and E. Valle, “Statistical learning approach for robust melanoma screening,” in SIBGRAPI, 2014, pp. 319-326.
 M. Carvalho, “Transfer schemes for deep learning in image classification,” Master's thesis, University of Campinas, 2015.
 M. Fornaciali, M. Carvalho, F. V. Bittencourt, S. Avila, and E. Valle, “Towards automated melanoma screening: Proper computer vision & reliable results,” arXiv:1604.04024, 2016.
 A. Menegola, M. Fornaciali, R. Pires, F. V. Bittencourt, S. Avila, and E. Valle, “Knowledge transfer for melanoma screening with deep learning,” in IEEE ISBI, 2017.
 O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” arXiv:1505.04597, 2015.
 K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556, 2014.
 N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, and J. R. Smith, “Deep learning, sparse coding, and svm for melanoma recognition in dermoscopy images,” in MLMI, 2015, pp. 118-126.
 A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115-118, 2017.
 G. Argenziano, H. P. Soyer, V. De Giorgi, D. Piccolo, P. Carli, M. Delfino et al., “Dermoscopy: a tutorial,” EDRA, Medical Publishing & New Media, 2002.
 L. Ballerini, R. B. Fisher, B. Aldridge, and J. Rees, “A color and texture based hierarchical k-nn approach to the classification of non-melanoma skin lesions,” in CMIA, 2013, pp. 63-86.