title: Learning pit pattern concepts for gastroenterological training.
abstract: In this article, we propose an approach to learn the characteristics of colonic mucosal surface structures, the so called pit patterns, commonly observed during high-magnification colonoscopy. Since the discrimination of the pit pattern types usually requires an experienced physician, an interesting question is whether we can automatically find a collection of images which most typically show a particular pit pattern characteristic. This is of considerable practical interest, since it is imperative for gastroenterological training to have a representative image set for the textbook descriptions of the pit patterns. Our approach exploits recent research on semantic image retrieval and annotation. This facilitates to learn a semantic space for the pit pattern concepts which eventually leads to a very natural formulation of our task.
authors: Dept. of Computer Sciences, Univ. of Salzburg, Austria.
authors: Roland
authors: R
authors: Kwitt
authors: Nikhil
authors: N
authors: Rasiwasia
authors: Nuno
authors: N
authors: Vasconcelos
authors: Andreas
authors: A
authors: Uhl
authors: Michael
authors: M
authors: Häfner
authors: Friedrich
authors: F
authors: Wrba
mesh_terms: D000465:Algorithms; D003113:Colonoscopy / Q000379:methods*; D003210:Concept Formation; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods*; D004501:Education, Medical / Q000379:methods; D004724:Endoscopy / Q000379:methods; D005762:Gastroenterology / Q000193:education* / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007414:Intestinal Neoplasms / Q000175:diagnosis / Q000473:pathology; D015233:Models, Statistical; D015203:Reproducibility of Results