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

We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, color) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyze the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources. Overall we find weak correlations of non-expert annotations with the diagnostic label, and low agreement between different annotation sources. Next we study multi-task learning (MTL) with the annotations as additional labels, and show that non-expert annotations improve the diagnostic performance of (ensembles of) state-of-the-art convolutional neural networks. We hope that our data