Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23626-6_35 | MICCAI (3) |
Keywords | Field | DocType |
considerable practical interest,pit pattern concept,semantic space,colonic mucosal surface structure,pit pattern,particular pit pattern characteristic,experienced physician,pit pattern type,representative image,semantic image retrieval,gastroenterological training | Annotation,Pattern recognition,Computer science,Concept learning,Image retrieval,Narrow-band imaging,Exploit,Natural language processing,Artificial intelligence,Confocal laser endomicroscopy,Semantic space | Conference |
Volume | Issue | ISSN |
14 | Pt 3 | 0302-9743 |
Citations | PageRank | References |
4 | 0.48 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R Kwitt | 1 | 448 | 35.15 |
| N Rasiwasia | 2 | 1173 | 34.61 |
| Nuno Vasconcelos | 3 | 5410 | 273.99 |
| Andreas Uhl | 4 | 1958 | 223.07 |
| M Häfner | 5 | 143 | 11.99 |
| F Wrba | 6 | 72 | 5.10 |