TY - GEN
T1 - Augmenting capsule endoscopy diagnosis
T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
AU - Seshamani, S.
AU - Kumar, R.
AU - Dassopoulos, T.
AU - Mullin, G.
AU - Hager, G.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84866619997&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-15745-5_56
DO - 10.1007/978-3-642-15745-5_56
M3 - Conference contribution
C2 - 20879347
AN - SCOPUS:84866619997
SN - 3642157440
SN - 9783642157448
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 454
EP - 462
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Y2 - 20 September 2010 through 24 September 2010
ER -