Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning

Hekler, Achim and Maron, Roman C. and Haggenmueller, Sarah and Schmitt, Max and Wies, Christoph and Utikal, Jochen S. and Meier, Friedegund and Hobelsberger, Sarah and Gellrich, Frank F. and Sergon, Mildred and Hauschild, Axel and French, Lars E. and Heinzerling, Lucie and Schlager, Justin G. and Ghoreschi, Kamran and Schlaak, Max and Hilke, Franz J. and Poch, Gabriela and Korsing, Soeren and Berking, Carola and Heppt, Markus and Erdmann, Michael and Haferkamp, Sebastian and Drexler, Konstantin and Schadendorf, Dirk and Sondermann, Wiebke and Goebeler, Matthias and Schilling, Bastian and Kather, Jakob N. and Krieghoff-Henning, Eva and Brinker, Titus J. (2024) Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning. JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY, 90 (5). pp. 1028-1031. ISSN 0190-9622, 1097-6787

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Item Type:	Article
Uncontrolled Keywords:	artificial intelligence; deep learning; dermatology; dermoscopy; diagnosis; diagnostic accuracy; melanoma; robustness; uncertainty estimation
Subjects:	600 Technology > 610 Medical sciences Medicine
Divisions:	Medicine > Lehrstuhl für Dermatologie und Venerologie
Depositing User:	Dr. Gernot Deinzer
Date Deposited:	29 Oct 2025 06:13
Last Modified:	29 Oct 2025 06:13
URI:	https://pred.uni-regensburg.de/id/eprint/65552

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