Maron, Roman C. ; Haggenmüller, Sarah ; von Kalle, Christof 
; Utikal, Jochen S. ; Meier, Friedegund ; Gellrich, Frank F. ; Hauschild, Axel ; French, Lars E. ; Schlaak, Max ; Ghoreschi, Kamran ; Kutzner, Heinz ; Heppt, Markus V. ; Haferkamp, Sebastian ; Sondermann, Wiebke ; Schadendorf, Dirk ; Schilling, Bastian ; Hekler, Achim ; Krieghoff-Henning, Eva ; Kather, Jakob N. ; Fröhling, Stefan ; Lipka, Daniel B. ; Brinker, Titus J.
; Utikal, Jochen S. ; Meier, Friedegund ; Gellrich, Frank F. ; Hauschild, Axel ; French, Lars E. ; Schlaak, Max ; Ghoreschi, Kamran ; Kutzner, Heinz ; Heppt, Markus V. ; Haferkamp, Sebastian ; Sondermann, Wiebke ; Schadendorf, Dirk ; Schilling, Bastian ; Hekler, Achim ; Krieghoff-Henning, Eva ; Kather, Jakob N. ; Fröhling, Stefan ; Lipka, Daniel B. ; Brinker, Titus J. 
Zusammenfassung
Background: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin cancer screening, should not change the diagnosis of such assistance systems. Objective: To quantify to what extent minor image perturbations affect ...
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