# Risk stratification of malignant melanoma using neural networks

arXiv: https://arxiv.org/abs/2306.06195 · HTML: https://mlsanity.com/paper/2306.06195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [On the use of Harrell's C for clinical risk prediction via random survival forests](https://mlsanity.com/paper/1507.03092/) (2015)
- [Automatic labeling of molecular biomarkers of whole slide immunohistochemistry images using fully convolutional networks](https://mlsanity.com/paper/1612.09420/) (2016)

## Abstract (arXiv)

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.