  In this paper we survey, analyze and criticize current art on automated
melanoma screening, reimplementing a baseline technique, and proposing two
novel ones. Melanoma, although highly curable when detected early, ends as one
of the most dangerous types of cancer, due to delayed diagnosis and treatment.
Its incidence is soaring, much faster than the number of trained professionals
able to diagnose it. Automated screening appears as an alternative to make the
most of those professionals, focusing their time on the patients at risk while
safely discharging the other patients. However, the potential of automated
melanoma diagnosis is currently unfulfilled, due to the emphasis of current
literature on outdated computer vision models. Even more problematic is the
irreproducibility of current art. We show how streamlined pipelines based upon
current Computer Vision outperform conventional models - a model based on an
advanced bags of words reaches an AUC of 84.6%, and a model based on deep
neural networks reaches 89.3%, while the baseline (a classical bag of words)
stays at 81.2%. We also initiate a dialog to improve reproducibility in our
community
