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J Invest Dermatol. 2018 Oct;138(10):2108-2110. doi: 10.1016/j.jid.2018.06.175.

Automated Classification of Skin Lesions: From Pixels to Practice.

Author information

1
Stanford School of Medicine, Stanford University, Stanford, California, USA.
2
Department of Electrical Engineering, Stanford University, Stanford, California, USA.
3
Department of Dermatology, Stanford University, Stanford, California, USA.
4
Department of Pathology, Stanford University, Stanford, California, USA.
5
Department of Dermatology, Stanford University, Stanford, California, USA. Electronic address: jmko@stanford.edu.

Abstract

The letters "Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset" and "Automated Dermatological Diagnosis: Hype or Reality?" highlight the opportunities, hurdles, and possible pitfalls with the development of tools that allow for automated skin lesion classification. The potential clinical impact of these advances relies on their scalability, accuracy, and generalizability across a range of diagnostic scenarios.

PMID:
30244720
DOI:
10.1016/j.jid.2018.06.175
[Indexed for MEDLINE]

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