Letter
Automated diagnosis of melanoma
Monika Janda,
Corresponding Author
Monika Janda
Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD
Correspondence:
[email protected]Search for more papers by this author H Peter Soyer,
H Peter Soyer
Dermatology Research Centre, University of Queensland Diamantina Institute, Brisbane, QLD
Princess Alexandra Hospital, Brisbane, QLD
Search for more papers by this author
Monika Janda,
Corresponding Author
Monika Janda
Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD
Correspondence:
[email protected]Search for more papers by this author H Peter Soyer,
H Peter Soyer
Dermatology Research Centre, University of Queensland Diamantina Institute, Brisbane, QLD
Princess Alexandra Hospital, Brisbane, QLD
Search for more papers by this author
First published: 16 October 2017
No abstract is available for this article.
References
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- 2Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115–118.
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- 4Aitken JF, Elwood M, Baade PD, et al. Clinical whole-body skin examination reduces the incidence of thick melanomas. Int J Cancer 2010; 126: 450–458.
- 5 International Skin Imaging Collaboration. ISBI 2016: skin lesion analysis towards melanoma detection [website]. https://challenge.kitware.com/#challenge/n/ISBI_2016%3A_Skin_Lesion_Analysis_Towards_Melanoma_Detection (accessed June 2017).
