A Semi-supervised Learning Method for Motility Disease Diagnostic

  • Santi Seguí
  • Laura Igual
  • Petia Radeva
  • Carolina Malagelada
  • Fernando Azpiroz
  • Jordi Vitrià
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

This work tackles the problem of learning a robust classification function from a very small sample set when a related but unlabeled data set is provided. To this end we define a new semi-supervised method that is based on a stability criterion. We successfully apply our proposal in the specific case of automatic diagnosis of intestinal motility disease using video capsule endoscopy. An experimental evaluation shows the viability to apply the proposed method in motility disfunction diagnosis.

Keywords

Feature Extraction Intestinal Motility Diseases Semi-Supervised Learning Suppor Vector Machine Wireless Capsule Video Endoscopy 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Santi Seguí
    • 1
  • Laura Igual
    • 1
  • Petia Radeva
    • 1
    • 2
  • Carolina Malagelada
    • 3
  • Fernando Azpiroz
    • 3
  • Jordi Vitrià
    • 1
    • 2
  1. 1.Computer Vision Center, Universitat Autònoma de Barcelona, BellaterraSpain
  2. 2.Computer Science Department, Universitat Autònoma de Barcelona, BellaterraSpain
  3. 3.Hospital de Vall d’Hebron, BarcelonaSpain

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