Leveraging the crowd for annotation of retinal images
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Raskar_Leveraging the crowd.pdf
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Author(s)
Leifman, George
Swedish, Tristan
Roesch, Karin
Raskar, Ramesh
Date Issued
November 2015
Journal
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/EMBC.2015.7320185
