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We present ENHANCE, an open dataset with multiple annotations to complementthe existing ISIC and PH2 skin lesion classification datasets. This datasetcontains annotations of visual ABC (asymmetry, border, colour) features fromnon-expert annotation sources: undergraduate students, crowd workers fromAmazon MTurk and classic image processing algorithms. In this paper we firstanalyse the correlations between the annotations and the diagnostic label ofthe lesion, as well as study the agreement between different annotationsources. Overall we find weak correlations of non-expert annotations with thediagnostic label, and low agreement between different annotation sources. Wethen study multi-task learning (MTL) with the annotations as additional labels,and show that non-expert annotations can improve (ensembles of)state-of-the-art convolutional neural networks via MTL. We hope that ourdataset can be used in further research into multiple annotations and/or MTL.All data and models are available on Github:https://github.com/raumannsr/ENHANCE.

ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification