Automated Classification of Skin Lesions: From Pixels to Practice

Akhila Narla; Brett Kuprel; Kavita Sarin; Roberto Novoa; Justin Ko

doi:10.1016/j.jid.2018.06.175

Automated Classification of Skin Lesions: From Pixels to Practice

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

Authors

Akhila Narla¹, Brett Kuprel², Kavita Sarin³, Roberto Novoa⁴, Justin Ko⁵

Affiliations

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.

PMID: 30244720
DOI: 10.1016/j.jid.2018.06.175

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.

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MeSH terms

Deep Learning
Dermoscopy*
Humans
Skin Neoplasms*