Hekler, Achim ; Kather, Jakob N. 
; Krieghoff-Henning, Eva ; Utikal, Jochen S. ; Meier, Friedegund ; Gellrich, Frank F. ; Upmeier zu Belzen, Julius ; French, Lars ; Schlager, Justin G. ; Ghoreschi, Kamran ; Wilhelm, Tabea ; Kutzner, Heinz ; Berking, Carola ; Heppt, Markus V. ; Haferkamp, Sebastian ; Sondermann, Wiebke ; Schadendorf, Dirk ; Schilling, Bastian ; Izar, Benjamin ; Maron, Roman ; Schmitt, Max ; Fröhling, Stefan ; Lipka, Daniel B. ; Brinker, Titus J.
; Krieghoff-Henning, Eva ; Utikal, Jochen S. ; Meier, Friedegund ; Gellrich, Frank F. ; Upmeier zu Belzen, Julius ; French, Lars ; Schlager, Justin G. ; Ghoreschi, Kamran ; Wilhelm, Tabea ; Kutzner, Heinz ; Berking, Carola ; Heppt, Markus V. ; Haferkamp, Sebastian ; Sondermann, Wiebke ; Schadendorf, Dirk ; Schilling, Bastian ; Izar, Benjamin ; Maron, Roman ; Schmitt, Max ; Fröhling, Stefan ; Lipka, Daniel B. ; Brinker, Titus J. 
Zusammenfassung
Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of ...
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