  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, colour) features from
non-expert annotation sources: undergraduate students, crowd workers from
Amazon MTurk and classic image processing algorithms. In this paper we first
analyse 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. We
then 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 our
dataset 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.
