Automatic Labeling of Colonoscopy Video for Cancer Detection

  • Fernando Vilariño
  • Gerard Lacey
  • Jiang Zhou
  • Hugh Mulcahy
  • Stephen Patchett
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)


The labeling of large quantities of medical video data by clinicians is a tedious and time consuming task. In addition, the labeling process itself is rigid, since it requires the expert’s interaction to classify image contents into a limited number of predetermined categories. This paper describes an architecture to accelerate the labeling step using eye movement tracking data. We report some initial results in training a Support Vector Machine (SVM) to detect cancer polyps in colonoscopy video, and a further analysis of their categories in the feature space using Self Organizing Maps (SOM). Our overall hypothesis is that the clinician’s eye will be drawn to the salient features of the image and that sustained fixations will be associated with those features that are associated with disease states.


Support Vector Machine Cancer Detection Polyp Detection Interest Operator Endoscopy Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Fernando Vilariño
    • 1
  • Gerard Lacey
    • 1
  • Jiang Zhou
    • 1
  • Hugh Mulcahy
    • 2
  • Stephen Patchett
    • 3
  1. 1.Computer Science Dept. Trinity College Dublin, Dublin 1Ireland
  2. 2.St. Vincent’s University Hospital, Elm park, Dublin 4Ireland
  3. 3.Beaumount Hospital, P.O. Box 1297 Beaumont Road, Dublin 9Ireland

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