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Our goal is to bridge human and machine intelligence in melanoma detection.We develop a classification system exploiting a combination of visualpre-processing, deep learning, and ensembling for providing explanations toexperts and to minimize false negative rate while maintaining high accuracy inmelanoma detection. Source images are first automatically segmented using aU-net CNN. The result of the segmentation is then used to extract imagesub-areas and specific parameters relevant in human evaluation, namely center,border, and asymmetry measures. These data are then processed by tailoredneural networks which include structure searching algorithms. Partial resultsare then ensembled by a committee machine. Our evaluation on the largest skinlesion dataset which is publicly available today, ISIC-2019, shows improvementin all evaluated metrics over a baseline using the original images only. Wealso showed that indicative scores computed by the feature classifiers canprovide useful insight into the various features on which the decision can bebased.

Minimizing false negative rate in melanoma detection and providing insight into the causes of classification