Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

• Verfasst von: Brinker, Titus Josef [VerfasserIn]
• Verfasst von: Enk, Alexander [VerfasserIn]
• Verfasst von: Kalle, Christof von [VerfasserIn]
• Titel: Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task
• Verf.angabe: Titus J. Brinker, Achim Hekler, Alexander H. Enk, Joachim Klode, Axel Hauschild, Carola Berking, Bastian Schilling, Sebastian Haferkamp, Dirk Schadendorf, Tim Holland-Letz, Jochen S. Utikal, Christof von Kalle, collaborators
• E-Jahr: 2019
• Jahr: 10 April 2019
• Umfang: 8 S.
• Fussnoten: Gesehen am 15.10.2019
• Titel Quelle: Enthalten in: European journal of cancer
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1965
• Jahr Quelle: 2019
• Band/Heft Quelle: 113(2019), Seite 47-54
• ISSN Quelle: 1879-0852
• DOI: doi:10.1016/j.ejca.2019.04.001
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Artificial intelligence
• Sach-SW: Melanoma
• Sach-SW: Skin cancer
• K10plus-PPN: 1678890464

Abstract

Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy.