Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Guy GP, Machlin S, Ekwueme DU, Yabroff KR. Prevalence and costs of skin cancer treatment in the US, 2002—2006 and 2007—2011. Am J Prev Med. 2015;48:183—7

2015

Earlier work this paper cites.

Gutman D, Codella NCF, Celebi E, Helba, B, Marchetti M, Mishra N, Halpern A. “Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)”. eprint arXiv:1605.01397. 2016

Original

2016

Earlier work this paper cites.

Carrera C, et al. “Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma: A Web-Based International Dermoscopy Society Study.” JAMA Dermatol. 2016 July 01; 152(7): 798—806

2016

Earlier work this paper cites.

Codella NCF, Nguyen B, Pankanti S, Gutman D, Helba B, Halpern A, Smith JR. “Deep learning ensembles for melanoma recognition in dermoscopy images” In: IBM Journal of Research and Development, vol. 61, no. 4/5, 2017

2017

Earlier work this paper cites.

Esteva A,Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. “Dermatologist-level classification of skin cancer with deep neural networks”. Nature, vol 542, pp 115—118. 2017

2017

Cited alongside, same era.

Menegola A, Tavares J, Fornaciali M, Li LT, Avila S, Valle E. “RECOD Titans at ISIC Challenge 2017”. 2017 International Symposium on Biomedical Imaging (ISBI) Challenge on Skin Lesion Analysis Towards Melanoma Detection. Available: https://arxiv.org/pdf/1703.04819.pdf

Original

2017

Cited alongside, same era.

Diaz, I.G. “Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions. 2017 International Symposium on Biomedical Imaging (ISBI) Challenge on Skin Lesion Analysis Towards Melanoma Detection.” Available: https://arxiv.org/abs/1703.01976

Original

2017

Cited alongside, same era.

Cancer Facts and Figures 2018. American Cancer Society. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2018/cancer-facts-and-figures-2018.pdf. Accessed May 3, 2018

2018

Marchetti M, et al. “Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images”. J Am Acad Dermatol. 2018 Feb;78(2):270—277

2018

Later among the works it cites.

Codella N, et al. “Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2017, hosted by the International Skin Imaging Collaboration (ISIC)”. IEEE International Symposium of Biomedical Imaging (ISBI) 2018

2018

Later among the works it cites.

Tschandl P, Rosendahl C, Kittler H. “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.” Sci Data. 2018 Aug 14;5:180161

2018

Later among the works it cites.

Tschandl P, Sinz C, Kittler H. “Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation.” Computers in Biology and Medicine, vol 104, pp 111—116, 2019