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Semi-supervised Deep Learning for Fully Convolutional Networks

  • Christoph BaurEmail author
  • Shadi Albarqouni
  • Nassir Navab
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.

Notes

Acknowledgements

We thank our clinical partners, in particular Dr. med. Paul Eichinger and Dr. med. Benedikt Wiestler, from the Neuroradiology Department of Klinikum Rechts der Isar for providing us with their MRI MS Lesion dataset. Further, we want to thank Rohde & Schwarz GmbH & Co KG for funding the project.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christoph Baur
    • 1
    Email author
  • Shadi Albarqouni
    • 1
  • Nassir Navab
    • 1
    • 2
  1. 1.Computer Aided Medical Procedures (CAMP)Technische Universität MünchenMunichGermany
  2. 2.Whiting School of EngineeringJohns Hopkins UniversityBaltimoreUSA

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