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Innovation in Biomedical Science and Engineering

Melanoma segmentation based on deep learning

References

  • Australian CC. Cancer Council to launch new research/failure to monitor highlights cancer risk, http://www.cancer.org.au/cancersmartlifestyle/SunSmart/Skincancer-factsandfigures.htm, 2010.
  • Green A, Martin N. etc., Computer image analysis in the diagnosis of melanoma. J Am Acad Dermatol. 1994;31:958–964.
  • Lee HC. Skin cancer diagnosis using hierarchical neural networks and fuzzy logic. Department of Computer Science, University of Missouri, Rolla, 1994.
  • Aitken JF, Pfitzner J, Battistutta D, et al. Reliability of computer image analysis of pigmented skin lesions of Australian adolescents. J. Cancer. 1996;78:252–257.
  • Chang Y, Stanley RJ, Moss RH, et al. A systematic heuristic approach for feature selection for melanoma discrimination using clinical images. Skin Res Technol. 2005;11:165–178.
  • She Z, Liu Y, Damatoa A. Combination of features from skin pattern and ABCD analysis for lesion classification. Skin Res Technol. 2007;13:25–33.
  • Fassihi N, Shanbehzadeh J, Sarrafzadeh A, et al. Melanoma diagnosis by the use of wavelet analysis based on morphological operators. Proceedings of the International Multiconference of Engineers and Computer Scientists. 16–18, 2011.
  • LeCun B, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278–2324.
  • Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks, Proc. BRATS-MICCAI, 2014.
  • Zikic I, Ioannou Y, Criminisi A, et al. Segmentation of brain tumor tissues with convolutional neural networks, Proc. BRATS-MICCAI, 2014.
  • Urban B, Bendszus M, Hamprecht FA, et al. Multi-modal brain tumor segmentation using deep convolutional neural networks, Proc. BRATS-MICCAI, 2014.
  • Pinheiro P, Collobert R. Recurrent convolutional neural networks for scene labeling, Proceedings of the 31st International Conference on Machine Learning, pp. 82–90, 2014.
  • Farabet C, Camille Couprie C, Najman L, et al. Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell. 2013;35:1915–1929.
  • Brosch T, Yoo Y, Tang L, et al. Deep convolutional encoder networks for multiple sclerosis lesion segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI; 2015. Vol. 9351, p. 3–11.
  • Kang K, Wang X. Fully convolutional neural networks for crowd segmentation, [Online]. Available: arXiv: 1411.4464, to be published, 2014.
  • Long ES, Shelhamer E, Darrell T. “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 3431–3440, 2015.
  • Ronneberger O, Fischer P, Brox T. “U-net: Convolutional networks for biomedical image segmentation,” in Proc. 18th Int. Conf. MICCAI, p. 8, 2015
  • Huang J, Jain V. Deep and wide multiscale recursive networks for robust image labeling arXiv preprint arXiv:1310.0354, 2013.
  • Amaral T, Silva LM, Alexandre LA, et al. Transfer learning using rotated image data to improve deep neural network performance, International Conference Image Analysis and Recognition, ICIAR: Image Analysis and Recognition pp. 290–300, 2014.
  • Badrinarayanan V, Handa A, Cipolla R. A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, arXiv preprint arXiv:1505.07293, 2015.
  • Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440, 2015.