Weakly Supervised Group-Wise Model Learning Based on Discrete Optimization

  • René Donner
  • Horst Wildenauer
  • Horst Bischof
  • Georg Langs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

In this paper we propose a method for the weakly supervised learning of sparse appearance models from medical image data based on Markov random fields (MRF). The models are learnt from a single annotated example and additional training samples without annotations. The approach formulates the model learning as solving a set of MRFs. Both the model training and the resulting model are able to cope with complex and repetitive structures. The weakly supervised model learning yields sparse MRF appearance models that perform equally well as those trained with manual annotations, thereby eliminating the need for tedious manual training supervision. Evaluation results are reported for hand radiographs and cardiac MRI slices.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • René Donner
    • 1
    • 2
  • Horst Wildenauer
    • 3
  • Horst Bischof
    • 2
  • Georg Langs
    • 1
  1. 1.Computational Image Analysis and Radiology Lab, Department of RadiologyMedical University of ViennaAustria
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyAustria
  3. 3.Pattern Recognition and Image Processing GroupVienna University of TechnologyAustria

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