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Semi–supervised Learning for Image Modality Classification

  • Alba García Seco de HerreraEmail author
  • Dimitrios Markonis
  • Ranveer Joyseeree
  • Roger Schaer
  • Antonio Foncubierta-Rodríguez
  • Henning Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9059)

Abstract

Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non–relevant items. Training datasets are often unevenly distributed regarding the classes resulting sometimes in a less than optimal classification performance. This article proposes a semi–supervised learning approach applied using a k–Nearest Neighbours (k–NN) classifier to exploit unlabelled data and to expand the training set. The algorithmic implementation is described and the method is evaluated on the ImageCLEFmed modality classification benchmark. Results show that this approach achieves an improved performance over supervised k–NN and Random Forest classifiers. Moreover, medical case–based retrieval also obtains higher performance when using the classified modalities as filter. This shows that image types can be classified well using visual information and they can then be used in a variety of applciations.

Keywords

Semi–supervised learning Medical image classification Crowdsourcing Case–based retrieval 

Notes

Acknowledgements

This work was partly supported by the EU \(7^{th}\) Framework Program in the context of the Khresmoi project (FP7–257528).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alba García Seco de Herrera
    • 1
    Email author
  • Dimitrios Markonis
    • 1
  • Ranveer Joyseeree
    • 1
    • 2
  • Roger Schaer
    • 1
  • Antonio Foncubierta-Rodríguez
    • 2
  • Henning Müller
    • 1
  1. 1.University of Applied Sciences Western Switzerland (HES–SO)SierreSwitzerland
  2. 2.Swiss Federal Institute of TechnologyZurichSwitzerland

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