ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
This work provides a dataset for researchers studying multiple annotations or multi-task learning in medical imaging, but it is incremental as it builds on existing datasets like ISIC and PH2.
The authors introduced ENHANCE, an open dataset with multiple annotations (from non-experts and algorithms) for skin lesion classification, finding weak correlations with diagnostic labels and low inter-annotator agreement, but showing that these annotations can improve state-of-the-art convolutional neural networks through multi-task learning.
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.