arXiv 1806.081742018-06cs.CV

Crowd disagreement about medical images is informative

Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Eindhoven University of Technology（埃因霍温理工大学）

Veronika Cheplygina, Josien P. W. Pluim

备注：: Accepted for publication at MICCAI LABELS 2018

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英文摘要：

Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotators may be informative, and thus removing it may not be the best strategy. As a proof of concept, we predict whether a skin lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd annotations of visual characteristics of that lesion. We compare using the mean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at \url{https://figshare.com/s/5cbbce14647b66286544}.

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