Maron, Roman C. ; Hekler, Achim ; Haggenmüller, Sarah ; von Kalle, Christof ; Utikal, Jochen S. ; Müller, Verena ; Gaiser, Maria ; Meier, Friedegund 
; Hobelsberger, Sarah ; Gellrich, Frank F. ; Sergon, Mildred ; Hauschild, Axel ; French, Lars E. ; Heinzerling, Lucie ; Schlager, Justin G. ; Ghoreschi, Kamran ; Schlaak, Max ; Hilke, Franz J. ; Poch, Gabriela ; Korsing, Sören ; Berking, Carola ; Heppt, Markus V. ; Erdmann, Michael ; Haferkamp, Sebastian ; Schadendorf, Dirk ; Sondermann, Wiebke ; Goebeler, Matthias ; Schilling, Bastian ; Kather, Jakob N. ; Fröhling, Stefan ; Lipka, Daniel B. ; Krieghoff-Henning, Eva ; Brinker, Titus J.
; Hobelsberger, Sarah ; Gellrich, Frank F. ; Sergon, Mildred ; Hauschild, Axel ; French, Lars E. ; Heinzerling, Lucie ; Schlager, Justin G. ; Ghoreschi, Kamran ; Schlaak, Max ; Hilke, Franz J. ; Poch, Gabriela ; Korsing, Sören ; Berking, Carola ; Heppt, Markus V. ; Erdmann, Michael ; Haferkamp, Sebastian ; Schadendorf, Dirk ; Sondermann, Wiebke ; Goebeler, Matthias ; Schilling, Bastian ; Kather, Jakob N. ; Fröhling, Stefan ; Lipka, Daniel B. ; Krieghoff-Henning, Eva ; Brinker, Titus J. 
Zusammenfassung
Background: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less ...
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