Digital imaging biomarkers feed machine learning for melanoma screening

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.