Digital imaging biomarkers feed machine learning for melanoma screening

Daniel S. Gareau, Joel Correa da Rosa, Sarah Yagerman, John A. Carucci, Nicholas Gulati, Ferran Hueto, Jennifer L. DeFazio, Mayte Suárez-Fariñas, Ashfaq Marghoob, James G. Krueger

Research output: Contribution to journalLetterpeer-review

23 Scopus citations


We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analysed with a set of 13 machine learning algorithms to generate an overall risk score that is called a Q-score. These methods were applied to a set of 120 “difficult” dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified. This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation. Importantly, we found strong spectral dependence of many imaging biomarkers in blue or red colour channels, suggesting the need to optimize spectral evaluation of pigmented lesions.

Original languageEnglish
Pages (from-to)615-618
Number of pages4
JournalExperimental Dermatology
Issue number7
StatePublished - Jul 2017


  • dermoscopy
  • imaging biomarkers
  • machine learning
  • machine vision
  • melanoma
  • pigmented lesion
  • screening
  • skin optics


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