Dermatologist-level classification of skin cancer with deep neural networks

American Cancer Society. Cancer facts & figures 2016. Atlanta, American Cancer Society 2016. http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-047079.pdf

Rogers, H. W. et al. Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population, 2012. JAMA Dermatology 151.10, 1081–1086 (2015)

Stern, R. S. Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch. Dermatol. 146, 279–282 (2010)

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015)

LeCun, Y. & Bengio, Y. In The Handbook of Brain Theory and Neural Networks (ed. Arbib, M. A. ) 3361.10 (MIT Press, 1995)

Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)

Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)

Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Proc. 32nd Int. Conference on Machine Learning 448–456 (2015)

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. Preprint at https://arxiv.org/abs/1512.00567 (2015)

Szegedy, C. et al. Going deeper with convolutions. Proc. IEEE Conference on Computer Vision and Pattern Recognition 1–9 (2015)

He, K. Zhang, X., Ren, S. & Sun J. Deep residual learning for image recognition. Preprint at https://arxiv.org/abs/1512.03385 (2015)

Masood, A. & Al-Jumaily, A. A. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 2013, 323268 (2013)

Cerwall, P. & Report, E. M. Ericssons mobility report https://www.ericsson.com/res/docs/2016/ericsson-mobility-report-2016.pdf (2016)

Rosado, B. et al. Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. Arch. Dermatol. 139, 361–367, discussion 366 (2003)

Burroni, M. et al. Melanoma computer-aided diagnosis: reliability and feasibility study. Clin. Cancer Res. 10, 1881–1886 (2004)

Kittler, H., Pehamberger, H., Wolff, K. & Binder, M. Diagnostic accuracy of dermoscopy. Lancet Oncol. 3, 159–165 (2002)

Codella, N. et al. In Machine Learning in Medical Imaging (eds Zhou, L., Wang, L., Wang, Q. & Shi, Y. ) 118–126 (Springer, 2015)

Gutman, D. et al. Skin lesion analysis toward melanoma detection. International Symposium on Biomedical Imaging (ISBI), (International Skin Imaging Collaboration (ISIC), 2016)

Binder, M. et al. Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network. Melanoma Res. 8, 261–266 (1998)

Menzies, S. W. et al. In Skin Cancer and UV Radiation (eds Altmeyer, P., Hoffmann, K. & Stücker, M. ) 1064–1070 (Springer, 1997)

Clark, W. H., et al. Model predicting survival in stage I melanoma based on tumor progression. J. Natl Cancer Inst. 81, 1893–1904 (1989)

Schindewolf, T. et al. Classification of melanocytic lesions with color and texture analysis using digital image processing. Anal. Quant. Cytol. Histol. 15, 1–11 (1993)

Ramlakhan, K. & Shang, Y. A mobile automated skin lesion classification system. 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 138–141 (2011)

Ballerini, L. et al. In Color Medical Image Analysis. (eds, Celebi, M. E. & Schaefer, G. ) 63–86 (Springer, 2013)

Deng, J. et al. Imagenet: A large-scale hierarchical image database. EEE Conference on Computer Vision and Pattern Recognition 248–255 (CVPR, 2009)

Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)

Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)

Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010)

Van der Maaten, L., & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

Abadi, M. et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. Preprint at https://arxiv.org/abs/1603.04467 (2016)