Detail

Keywords

Dermoscopy; Diagnostic Imaging; Early Detection of Cancer; Image Interpretation, Computer-Assisted; Neural Networks (Computer); Nevi and Melanomas

Selected publications

1. Katsch, F., Rinner, C. and Tschandl, P. (2022) ‘Comparison of convolutional neural network architectures for robustness against common artefacts in dermatoscopic images’, Dermatology Practical & Conceptual, p. e2022126. Available at: http://dx.doi.org/10.5826/dpc.1203a126.
2. Wesinger, A. et al. (2022) ‘Application of an interactive diagnosis ranking algorithm in a simulated vignette-based environment for general dermatology’, Dermatology Practical & Conceptual, p. e2022117. Available at: http://dx.doi.org/10.5826/dpc.1203a117.
3. Tschandl, P. et al. (2020) ‘Human–computer collaboration for skin cancer recognition’, Nature Medicine, 26(8), pp. 1229–1234. Available at: http://dx.doi.org/10.1038/s41591-020-0942-0.
4. Tschandl, P. et al. (2019) ‘Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study’, The Lancet Oncology, 20(7), pp. 938–947. Available at: http://dx.doi.org/10.1016/s1470-2045(19)30333-x.
5. Tschandl, P., Rosendahl, C. & Kittler, H., 2018. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5, p.180161. Available at: http://dx.doi.org/10.1038/sdata.2018.161.