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Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning

  • Matthias Perkonigg
  • Johannes Hofmanninger
  • Björn Menze
  • Marc-André Weber
  • Georg Langs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we present a transfer learning approach using convolutional neural networks to detect bone lesions in computed tomography imaging data. We compare different learning approaches, and demonstrate that pretraining a convolutional neural network on natural images improves detection accuracy. Also, we describe a patch extraction strategy which encodes different information into each input channel of the networks. We train and evaluate our approach on a dataset with 660 annotated bone lesions, and show how the resulting marker map high-lights lesions in computed tomography imaging data.

Notes

Acknowledgement

This work was supported by the Austrian Science Fund (FWF) project number I2714-B31.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Matthias Perkonigg
    • 1
  • Johannes Hofmanninger
    • 1
  • Björn Menze
    • 2
  • Marc-André Weber
    • 3
  • Georg Langs
    • 1
  1. 1.Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided TherapyMedical University of ViennaViennaAustria
  2. 2.Institute of Biomedical Engineering, Image-based Biomedical ModellingTechnical University of MunichMunichGermany
  3. 3.Institute of Diagnostic and Interventional RadiologyUniversity Medical Center RostockRostockGermany

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