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Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection

  • Michael GadermayrEmail author
  • Barbara Mara Klinkhammer
  • Peter Boor
  • Dorit Merhof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

Approaches for detecting regions of interest in biomedical image data mostly assume that a large amount of annotated training data is available. Certainly, for unchanging problem definitions, the acquisition of large annotated data is time consuming, yet feasible. However, distinct practical problems with large training corpi arise if variability due to different imaging conditions or inter-personal variations lead to significant changes in the image representation. To circumvent these issues, we investigate a classifier learning scenario which requires a small amount of positive annotation data only. Contrarily to previous approaches which focus on methodologies to explicitly or implicitly deal with specific classification scenarios (such as one-class classification), we show that existing supervised classification models can handle a changed setting effectively without any specific modifications.

Keywords

Training Data Image Representation Negative Class Classifier Training Positive Class 
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.

Notes

Acknowledgments

This work was supported by the German Research Foundation (DFG), grant number ME3737/3-1.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Michael Gadermayr
    • 1
    Email author
  • Barbara Mara Klinkhammer
    • 2
  • Peter Boor
    • 2
  • Dorit Merhof
    • 1
  1. 1.Aachen Center for Biomedical Image Analysis, Visualization and Exploration (ACTIVE), Institute of Imaging and Computer VisionRWTH Aachen UniversityAachenGermany
  2. 2.Institute of PathologyUniversity Hospital Aachen, RWTH Aachen UniversityAachenGermany

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