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Detail

Philipp Tschandl
Priv.Doz. Dr. Philipp Tschandl, PhD

Department of Dermatology
Position: Doctor-in-training

ORCID: 0000-0003-0391-7810

Further Information

Keywords

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

Research group(s)

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

  1. 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. Available at: http://dx.doi.org/10.1016/S1470-2045(19)30333-X.
  2. Tschandl, P. et al., 2019. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. JAMA Dermatology, 155(1), p.58. Available at: http://dx.doi.org/10.1001/jamadermatol.2018.4378.
  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.