Risk stratification of malignant melanoma using neural networks
Julian Burghoff Leonhard Ackermann Younes Salahdine Veronika Bram Katharina Wunderlich Julius Balkenhol Thomas Dirschka Hanno Gottschalk
In order to improve the detection and classification of malignant melanoma,
this paper describes an image-based method that can achieve AUROC values of up
to 0.78 without additional clinical information. Furthermore, the importance of
the domain gap between two different image sources is considered, as it is
important to create usability independent of hardware components such as the
high-resolution scanner used. Since for the application of machine learning
methods, alterations of scanner-specific properties such as brightness,
contrast or sharpness can have strong (negative) effects on the quality of the
prediction methods, two ways to overcome this domain gap are discussed in this
paper.