Risk stratification of malignant melanoma using neural networks

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,...

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Bibliographic Details
Published in:arXiv.org 2023-05
Main Authors: Burghoff, Julian, Ackermann, Leonhard, Younes Salahdine, Bram, Veronika, Wunderlich, Katharina, Balkenhol, Julius, Dirschka, Thomas, Gottschalk, Hanno
Format: Article
Language:English
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Machine learning
Neural networks
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Summary: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.
ISSN:2331-8422