Priv.Doz. Dr. Philipp Tschandl, PhD
Department of Dermatology
Position: Research Associate (Postdoc)
ORCID: 0000-0003-0391-7810
Keywords
Dermoscopy; Diagnostic Imaging; Early Detection of Cancer; Image Interpretation, Computer-Assisted; Neural Networks (Computer); Nevi and Melanomas
Research group(s)
- Vienna Dermatologic Imaging Research
Research Area: The ViDIR group focuses on a variety of topics related to diagnosis of skin diseases. We harness diverse computational, physical and psychological techniques to enhance future diagnostic abilities of humans and devices.
Members:
Research interests
To understand and develop automated systems for dermatologic image analysis.
Techniques, methods & infrastructure
Structured digital dermatopathologic slide scans and dermatoscopic image data originating from different sources are used for research.
Grants
- Die Rolle aktivierender BRAF Mutationen in Nävi aus welchen Melanome entstanden sind (2011)
Source of Funding: Margarethe Hehberger Stiftung,
Principal Investigator
Selected publications
- 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.
- 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.
- 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.
- 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.
- 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.