Augmenting capsule endoscopy diagnosis: a similarity learning approach

Authors:
S. Seshamani;R. Kumar;T. Dassopoulos;G. Mullin;G. Hager
Affiliations:
Department of Computer Science, Johns Hopkins University, Baltimore, MD;Department of Computer Science, Johns Hopkins University, Baltimore, MD;Washington University, School of Medicine St. Louis, MO;Johns Hopkins Hospital, Baltimore, MD;Department of Computer Science, Johns Hopkins University, Baltimore, MD
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

Citing 7
Cited 0

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Local similarity discriminant analysis

Proceedings of the 24th international conference on Machine learning
On Pairwise Kernels: An Efficient Alternative and Generalization Analysis

PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
More generality in efficient multiple kernel learning

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Similarity-based Classification: Concepts and Algorithms

The Journal of Machine Learning Research
A Meta Registration Framework for Lesion Matching

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Color and texture descriptors

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

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.