Augmenting capsule endoscopy diagnosis: a similarity learning approach.

S. Seshamani, R. Kumar, T. Dassopoulos, Gerard Mullin, Gregory Hager

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The current procedure for diagnosis of Crohn's disease (CD) from Capsule Endoscopy is a tedious manual process which requires the clinician to visually inspect large video sequences for matching and categorization of diseased areas (lesions). Automated methods for matching and classification can help improve this process by reducing diagnosis time and improving consistency of categorization. In this paper, we propose a novel SVM-based similarity learning method for distinguishing between correct and incorrect matches in Capsule Endoscopy (CE). We also show that this can be used in conjunction with a voting scheme to categorize lesion images. Results show that our methods outperform standard classifiers in discriminating similar from dissimilar lesion images, as well as in lesion categorization. We also show that our methods drastically reduce the complexity (training time) by requiring only one half of the data for training, without compromising the accuracy of the classifier.

Original languageEnglish (US)
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages454-462
Number of pages9
Volume13
EditionPt 2
StatePublished - 2010

Fingerprint

Capsule Endoscopy
Learning
Politics
Crohn Disease

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Seshamani, S., Kumar, R., Dassopoulos, T., Mullin, G., & Hager, G. (2010). Augmenting capsule endoscopy diagnosis: a similarity learning approach. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 13, pp. 454-462)

Augmenting capsule endoscopy diagnosis : a similarity learning approach. / Seshamani, S.; Kumar, R.; Dassopoulos, T.; Mullin, Gerard; Hager, Gregory.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. p. 454-462.

Research output: Chapter in Book/Report/Conference proceedingChapter

Seshamani, S, Kumar, R, Dassopoulos, T, Mullin, G & Hager, G 2010, Augmenting capsule endoscopy diagnosis: a similarity learning approach. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 13, pp. 454-462.
Seshamani S, Kumar R, Dassopoulos T, Mullin G, Hager G. Augmenting capsule endoscopy diagnosis: a similarity learning approach. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 13. 2010. p. 454-462
Seshamani, S. ; Kumar, R. ; Dassopoulos, T. ; Mullin, Gerard ; Hager, Gregory. / Augmenting capsule endoscopy diagnosis : a similarity learning approach. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. pp. 454-462
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