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Role of Task Complexity and Training in Crowdsourced Image Annotation

  • Nadine S. Schaadt
  • Anne Grote
  • Germain Forestier
  • Cédric Wemmert
  • Friedrich Feuerhake
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11039)

Abstract

Accurate annotation of anatomical structures or pathological changes in microscopic images is an important task in computational pathology. Crowdsourcing holds promise to address this demand, but so far feasibility has only be shown for simple tasks and not for high-quality annotation of complex structures which is often limited by shortage of experts. Third-year medical students participated in solving two complex tasks, labeling of images and delineation of relevant image objects in breast cancer and kidney tissue. We evaluated their performance and addressed the requirements of task complexity and training phases. Our results show feasibility and a high agreement between students and experts. The training phase improved accuracy of image labeling.

Keywords

Crowdsourcing Human decision making Image classification Image delineation Digital pathology Annotation 

Notes

Acknowledgements

We thank all students for contribution; M. Temerinac-Ott, Icube; R. Schönmeyer, C. Vanegas, Definiens for help in data selection; G. Stiller, M. Behrends, Peter L. Reichertz Institute for Medical Informatics; and A.-K. Rieke for the video.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Nadine S. Schaadt
    • 1
  • Anne Grote
    • 1
  • Germain Forestier
    • 2
  • Cédric Wemmert
    • 3
  • Friedrich Feuerhake
    • 1
    • 4
  1. 1.Institute for PathologyHannover Medical SchoolHannoverGermany
  2. 2.IRIMAS, University of Haute AlsaceMulhouseFrance
  3. 3.ICube, University of StrasbourgIllkirchFrance
  4. 4.Institute for NeuropathologyUniversity Clinic FreiburgFreiburg im BreisgauGermany

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