Leveraging the crowd for annotation of retinal images
Author(s)Leifman, George; Swedish, Tristan; Roesch, Karin; Raskar, Ramesh
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Medical data presents a number of challenges. It tends to be unstructured, noisy and protected. To train algorithms to understand medical images, doctors can label the condition associated with a particular image, but obtaining enough labels can be difficult. We propose an annotation approach which starts with a small pool of expertly annotated images and uses their expertise to rate the performance of crowd-sourced annotations. In this paper we demonstrate how to apply our approach for annotation of large-scale datasets of retinal images. We introduce a novel data validation procedure which is designed to cope with noisy ground-truth data and with non-consistent input from both experts and crowd-workers.
DepartmentMassachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Institute of Electrical and Electronics Engineers (IEEE)
Leifman, George et al. “Leveraging the Crowd for Annotation of Retinal Images.” 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 25-29 August, 2015, Milan, Italy, IEEE, 2015.
Author's final manuscript