A patient-centric dataset of images and metadata for identifying melanomas using clinical context
نویسندگان
چکیده
Abstract Prior skin image datasets have not addressed patient-level information obtained from multiple lesions the same patient. Though artificial intelligence classification algorithms achieved expert-level performance in controlled studies examining single images, practice dermatologists base their judgment holistically on 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 an identifier allowing patient be mapped one another. This contextual is frequently used by clinicians diagnose melanoma especially useful ruling out false positives patients with many atypical nevi. represents 2,056 (20.8% at least melanoma, 79.2% zero melanomas) three continents average of 16 per patient, consisting 33,126 dermoscopic images 584 (1.8%) histopathologically confirmed melanomas compared benign mimickers.
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ژورنال
عنوان ژورنال: Scientific Data
سال: 2021
ISSN: ['2052-4463']
DOI: https://doi.org/10.1038/s41597-021-00815-z