Quality Classification of Microscopic Imagery with Weakly Supervised Learning

  • Xinghua Lou
  • Luca Fiaschi
  • Ullrich Koethe
  • Fred A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)


In this post-genomic era, microscopic imaging is playing a crucial role in biomedical research and important information is to be discovered by quantitatively mining the resulting massive imagery databases. To this end, an important prerequisite is robust, high quality imagery databases. This is because defect images will jeopardize downstream tasks such as feature extraction and statistical analysis, yielding misleading results or even false conclusions. This paper presents a weakly supervised learning framework to tackle this problem. Our framework resembles a cascade of classifiers with feature and similarity measure designed for both global and local defects. We evaluated the framework on a database of images and obtained a 96.9% F-score for the important normal class. Click-and-play open source software is provided.


Training Image Outlier Detection Regional Defect Defect Image Original Feature Space 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xinghua Lou
    • 1
  • Luca Fiaschi
    • 1
  • Ullrich Koethe
    • 1
  • Fred A. Hamprecht
    • 1
  1. 1.HCI, IWRUniversity of HeidelbergGermany

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