A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context

Understand

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.