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In this paper we survey, analyze and criticize current art on automatedmelanoma screening, reimplementing a baseline technique, and proposing twonovel ones. Melanoma, although highly curable when detected early, ends as oneof the most dangerous types of cancer, due to delayed diagnosis and treatment.Its incidence is soaring, much faster than the number of trained professionalsable to diagnose it. Automated screening appears as an alternative to make themost of those professionals, focusing their time on the patients at risk whilesafely discharging the other patients. However, the potential of automatedmelanoma diagnosis is currently unfulfilled, due to the emphasis of currentliterature on outdated computer vision models. Even more problematic is theirreproducibility of current art. We show how streamlined pipelines based uponcurrent Computer Vision outperform conventional models - a model based on anadvanced bags of words reaches an AUC of 84.6%, and a model based on deepneural 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 ourcommunity

Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results