Detail

Keywords

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

Selected publications

1. Tschandl, P. et al., 2020. Human–computer collaboration for skin cancer recognition. Nature Medicine. Available at: http://dx.doi.org/10.1038/s41591-020-0942-0.
2. 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.
3. Tschandl, P., Sinz, C. & Kittler, H., 2019. Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation. Computers in Biology and Medicine, 104, pp.111–116. Available at: http://dx.doi.org/10.1016/j.compbiomed.2018.11.010.
4. Tschandl, P. et al., 2018. Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features. British Journal of Dermatology. Available at: http://dx.doi.org/10.1111/bjd.17189.
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.