Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis

Preprint English OPEN
Valle, Eduardo; Fornaciali, Michel; Menegola, Afonso; Tavares, Julia; Bittencourt, Flávia Vasques; Li, Lin Tzy; Avila, Sandra;
(2017)
  • Subject: Computer Science - Computer Vision and Pattern Recognition
Deep learning fostered a leap ahead in automated skin lesion analysis in the last two years. Those models are expensive to train and difficult to parameterize. Objective: We investigate methodological issues for designing and evaluating deep learning models for skin les... View more
  • References (38)
    38 references, page 1 of 4

    [1] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015.

    [2] N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler et al., “Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC),” arXiv preprint arXiv:1710.05006, 2017.

    [3] A. Menegola, J. Tavares, M. Fornaciali, L. T. Li, S. Avila, and E. Valle, “RECOD Titans at ISIC Challenge 2017,” arXiv preprint arXiv:1703.04819, 2017.

    [4] M. E. Celebi, H. A. Kingravi, B. Uddin, H. Iyatomi, Y. A. Aslandogan, W. V. Stoecker, and R. H. Moss, “A methodological approach to the classification of dermoscopy images,” Computerized Medical Imaging and Graphics, vol. 31, no. 6, pp. 362-373, 2007.

    [5] H. Iyatomi, H. Oka, M. E. Celebi, M. Hashimoto, M. Hagiwara, M. Tanaka, and K. Ogawa, “An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm,” Computerized Medical Imaging and Graphics, vol. 32, no. 7, pp. 566-579, 2008.

    [6] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. der Laak, B. G. Clara, and I. Sa´nchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60-88, 2017.

    [7] M. Fornaciali, M. Carvalho, F. V. Bittencourt, S. Avila, and E. Valle, “Towards automated melanoma screening: Proper computer vision & reliable results,” arXiv preprint arXiv:1604.04024, 2016.

    [8] S. Sabbaghi, M. Aldeen, and R. Garnavi, “A deep bag-of-features model for the classification of melanomas in dermoscopy images,” in IEEE Engineering in Medicine and Biology Society, 2016, pp. 1369-1372.

    [9] E. Nasr-Esfahani, S. Samavi, N. Karimi, S. M. R. Soroushmehr, M. H. Jafari, K. Ward, and K. Najarian, “Melanoma detection by analysis of clinical images using convolutional neural network,” in IEEE Engineering in Medicine and Biology Society, 2016, pp. 1373-1376.

    [10] X. Jia and L. Shen, “Skin lesion classification using class activation map,” arXiv preprint arXiv:1703.01053, 2017.

  • Related Research Results (2)
  • Metrics
Share - Bookmark