Περίληψη:
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.
Συγγραφείς:
Rotemberg, Veronica
Kurtansky, Nicholas
Betz-Stablein, Brigid
and Caffery, Liam
Chousakos, Emmanouil
Codella, Noel and
Combalia, Marc
Dusza, Stephen
Guitera, Pascale
Gutman, David
and Halpern, Allan
Helba, Brian
Kittler, Harald
Kose, Kivanc
and Langer, Steve
Lioprys, Konstantinos
Malvehy, Josep and
Musthaq, Shenara
Nanda, Jabpani
Reiter, Ofer
Shih, George
and Stratigos, Alexander
Tschandl, Philipp
Weber, Jochen and
Soyer, H. Peter
