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Do We Need Annotation Experts? A Case Study in Celiac Disease Classification

  • Roland Kwitt
  • Sebastian Hegenbart
  • Nikhil Rasiwasia
  • Andreas Vécsei
  • Andreas Uhl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Inference of clinically-relevant findings from the visual appearance of images has become an essential part of processing pipelines for many problems in medical imaging. Typically, a sufficient amount labeled training data is assumed to be available, provided by domain experts. However, acquisition of this data is usually a time-consuming and expensive endeavor. In this work, we ask the question if, for certain problems, expert knowledge is actually required. In fact, we investigate the impact of letting non-expert volunteers annotate a database of endoscopy images which are then used to assess the absence/presence of celiac disease. Contrary to previous approaches, we are not interested in algorithms that can handle the label noise. Instead, we present compelling empirical evidence that label noise can be compensated by a sufficiently large corpus of training data, labeled by the non-experts.

Keywords

Celiac Disease Local Binary Pattern Image Representation Training Corpus Fisher Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roland Kwitt
    • 1
  • Sebastian Hegenbart
    • 1
  • Nikhil Rasiwasia
    • 3
  • Andreas Vécsei
    • 2
  • Andreas Uhl
    • 1
  1. 1.Department of Computer ScienceUniversity of SalzburgAustria
  2. 2.St. Anna Children’s HospitalMedical University ViennaAustria
  3. 3.Yahoo Labs!BangaloreIndia

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