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Copyright and Attribution
The images and metadata of the "ISIC 2019: Training" data are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC).
The "ISIC 2019: Training" data includes content from several copyright holders. To comply with the attribution requirements of the CC-BY-NC license, the aggregate "ISIC 2019: Training" data must be cited as:
ISIC 2019 data is provided courtesy of the following sources:
BCN_20000 Dataset: (c) Department of Dermatology, Hospital Clínic de Barcelona
HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; https://doi.org/10.1038/sdata.2018.161
MSK Dataset: (c) Anonymous; https://arxiv.org/abs/1710.05006 ; https://arxiv.org/abs/1902.03368
When referencing this dataset in your own manuscripts and publications, please use the following full citations:
 Tschandl P., Rosendahl C. & Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi.10.1038/sdata.2018.161 (2018)
 Noel C. F. Codella, David Gutman, M. Emre Celebi, Brian Helba, Michael A. Marchetti, Stephen W. Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, Allan Halpern: “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)”, 2017; arXiv:1710.05006.
 Marc Combalia, Noel C. F. Codella, Veronica Rotemberg, Brian Helba, Veronica Vilaplana, Ofer Reiter, Allan C. Halpern, Susana Puig, Josep Malvehy: “BCN20000: Dermoscopic Lesions in the Wild”, 2019; arXiv:1908.02288.
Dermatoscopic images of the most common classes of skin lesions: Actinic Keratosis, Squamous Cell Carcinoma, Basal Cell Carcinoma, Seborrheic Keratosis, Solar Lentigo, Dermatofibroma, Nevi, Melanoma, and Vascular Lesions. Images are made available from the Department of Dermatology of the Hospital Clínic de Barcelona for the preparation of the ISIC Challenge 2019.