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.
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Kwitt, R., Rasiwasia, N., Vasconcelos, N., Uhl, A., Häfner, M., Wrba, F. (2011). Learning Pit Pattern Concepts for Gastroenterological Training. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_35
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DOI: https://doi.org/10.1007/978-3-642-23626-6_35
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