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A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Veronica Rotemberg # 1Nicholas Kurtansky # 2Brigid Betz-Stablein 3Liam Caffery 3Emmanouil Chousakos 2 4Noel Codella 5Marc Combalia 6Stephen Dusza 2Pascale Guitera 7David Gutman 8Allan Halpern 2Brian Helba 9Harald Kittler 10Kivanc Kose 2Steve Langer 11Konstantinos Lioprys 4Josep Malvehy 6Shenara Musthaq 2 12Jabpani Nanda 2 13Ofer Reiter 2 14George Shih 15Alexander Stratigos 4Philipp Tschandl 10Jochen Weber 2H Peter Soyer 3


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 (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.

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