Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72847-4_38 | IbPRIA (1) |
Keywords | Field | DocType |
cancer detection,support vector machine,medical video data,initial result,cancer polyp,self organizing maps,image content,eye movement,colonoscopy video,disease state,feature space | Computer vision,Feature vector,Colonoscopy,Fixation (psychology),Pattern recognition,Computer science,Support vector machine,Self-organizing map,Cancer detection,Eye movement,Tracking data,Artificial intelligence | Conference |
Volume | ISSN | Citations |
4477 | 0302-9743 | 3 |
PageRank | References | Authors |
0.80 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Vilariño | 1 | 263 | 22.08 |
| Gerard Lacey | 2 | 171 | 22.17 |
| Jiang Zhou | 3 | 41 | 13.69 |
| Hugh Mulcahy | 4 | 12 | 1.83 |
| Stephen Patchett | 5 | 12 | 1.83 |