Risk stratification of malignant melanoma using neural networks
This work addresses risk stratification for melanoma patients, but it is incremental as it focuses on overcoming domain gaps in existing image-based methods.
The paper tackled the problem of detecting and classifying malignant melanoma from images, achieving an AUROC of up to 0.78 without clinical data, and addressed domain gaps between different image sources to improve usability across hardware.
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.