Official dataset of the SIIM-ISIC Melanoma Classification Challenge

The dataset contains 33,126 dermoscopic training images of unique benign and malignant skin lesions from over 2,000 patients. Each image is associated with one of these individuals using a unique patient identifier. All malignant diagnoses have been confirmed via histopathology, and benign diagnoses have been confirmed using either expert agreement, longitudinal follow-up, or histopathology. A thorough publication describing all features of this dataset is available in the form of a pre-print that has not yet undergone peer review.

The dataset was generated by the International Skin Imaging Collaboration (ISIC) and images are from the following sources: Hospital Clínic de Barcelona, Medical University of Vienna, Memorial Sloan Kettering Cancer Center, Melanoma Institute Australia, University of Queensland, and the University of Athens Medical School.

The dataset was curated for the SIIM-ISIC Melanoma Classification Challenge hosted on Kaggle during the Summer of 2020.

DOI: https://doi.org/10.34970/2020-ds01

*The newer version of the DICOM files are provided to avoid potential errors stemming from readers implementing a strict DICOM verification, as implemented in http://dclunie.com/dicom3tools/dciodvfy.html.

Citing the 2020 dataset:

To comply with the attribution requirements of the CC-BY-NC license, the aggregate “ISIC 2020” data must be cited as:

International Skin Imaging Collaboration. SIIM-ISIC 2020 Challenge Dataset. International Skin Imaging Collaboration https://doi.org/10.34970/2020-ds01 (2020).

Creative Commons Attribution-Non Commercial 4.0 International License.

The dataset was generated by the International Skin Imaging Collaboration (ISIC) and images are from the following sources: Hospital Clínic de Barcelona, Medical University of Vienna, Memorial Sloan Kettering Cancer Center, Melanoma Institute Australia, The University of Queensland, and the University of Athens Medical School.

You should have received a copy of the license along with this work.

If not, see https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt.

When referencing this dataset in your own manuscripts and publications, please use the following full citation:

[1] Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Langer, S., Lioprys, K., Malvehy, J., Musthaq, S., Nanda, J., Reiter, O., Shih, G., Stratigos, A., Tschandl, P., Weber, J. & Soyer, P. A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci Data 8, 34 (2021). https://doi.org/10.1038/s41597-021-00815-z

Organizers

Sponsors

• The International Skin Imaging Collaboration (ISIC)

• Society for Imaging Informatics in Medicine (SIIM)

Clinical Chairs

• Peter Soyer (The University of Queensland, Dermatology Research Centre, Brisbane, AUS)
• Allan Halpern (Memorial Sloan Kettering Cancer Center, New York City, NY, USA)
• Pascale Guitera (Melanoma Institute Australia)

Computer Vision Chairs

• Noel C. F. Codella, Ph.D.

Challenge Co-Chairs

• Marc Combalia, M.S. (Fundació Clínic per a la Recerca Biomèdica, Barcelona, Spain)
• Veronica Rotemberg, M.D., Ph.D. (Memorial Sloan Kettering Cancer Center, New York City, NY, USA)

Partners

• SIIM:

  • George Shih
  • Steve Langer
  • Anna Zawacki

• Memorial Sloan Kettering Cancer Center:

  • Jochen Weber
  • Nick Kurtansky
  • Allan Halpern
  • Steve Dusza
  • Veronica Rotemberg

• Hospital Clinic, Barcelona:

  • Josep Malvehy
  • Marc Combalia

• Medical University of Vienna:

  • Harald Kittler
  • Philipp Tschandl

• Emory University:

  • David Gutman

• The University of Queensland:

  • Peter Soyer
  • Brigid Betz-Stablein

• Melanoma Institute Australia and Sydney Melanoma Diagnostic Center:

  • Pascale Guitera

• University of Athens:

  • Kontantinos Lioprys
  • Alexander Stratigos