Prior skin image datasets have not addressed patient-level information
obtained from multiple skin lesions from the same patient. Though artificial
intelligence classification algorithms have achieved expert-level performance
in controlled studies examining single images, in practice dermatologists base
their judgment holistically from multiple lesions on the same patient. The 2020
SIIM-ISIC Melanoma Classification challenge dataset described herein was
constructed to address this discrepancy between prior challenges and clinical
practice, providing for each image in the dataset an identifier allowing
lesions from the same patient to be mapped to one another. This patient-level
contextual information is frequently used by clinicians to diagnose melanoma
and is especially useful in ruling out false positives in patients with many
atypical nevi. The dataset represents 2,056 patients from three continents with
an average of 16 lesions per patient, consisting of 33,126 dermoscopic images
and 584 histopathologically confirmed melanomas compared with benign melanoma
mimickers.

2020 SIIM-ISIC Melanoma Classification challenge dataset was developed to provide patient-level contextual information for dermatologists’ holistic judgment on melanoma, containing 33,126 dermoscopic images and 584 confirmed melanomas among 2,056 patients.