Paper Info
Title
A machine learning framework using SOMs: applications in the intestinal motility assessment
Abstract
Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.
Year
DOI
Venue
2006
10.1007/11892755_19
CIARP
Keywords
Field
DocType
intestinal motility assessment,automatic detection,clinical environment,wireless capsule video endoscopy,different learning stage,small bowel motility assessment,self organized maps,intestinal contraction,rf signal,novel clinical methodology,machine learning
Computer vision,Computer science,Visualization,Support vector machine,Image processing,Artificial intelligence,Capsule endoscopy,Machine learning,Small bowel motility,Self organized map
Conference
Volume
ISSN
ISBN
4225
0302-9743
3-540-46556-1
Citations 
PageRank 
References 
4
0.49
5
Authors
5
Name
Order
Citations
PageRank
Fernando Vilariño126322.08
Panagiota Spyridonos222217.43
Jordi Vitrià373798.14
Carolina Malagelada4445.77
Petia Radeva51684153.53