Paper Info
Title
A Clinical Decision Support Framework For Incremental Polyps Classification In Virtual Colonoscopy
Abstract
We present in this paper a novel dynamic learning method for classifying polyp candidate detections in Computed Tomographic Colonography (CTC) using an adaptation of the Least Square Support Vector Machine (LS-SVM). The proposed technique, called Weighted Proximal Support Vector Machines (WP-SVM), extends the offline capabilities of the SVM scheme to address practical CTC applications. Incremental data are incorporated in the WP-SVM as a weighted vector space, and the only storage requirements are the hyper-plane parameters. WP-SVM performance evaluation based on 169 clinical CTC cases using a 3D computer-aided diagnosis (CAD) scheme for feature reduction comparable favorably with previously published CTC CAD studies that have however involved only binary and offline classification schemes. The experimental results obtained from iteratively applying WP-SVM to improve detection sensitivity demonstrate its viability for incremental learning, thereby motivating further follow on research to address a wider range of true positive subclasses such as pedunculated, sessile, and flat polyps, and over a wider range of false positive subclasses such as folds, stool, and tagged materials.
Year
DOI
Venue
2010
10.3390/a3010001
ALGORITHMS
Keywords
Field
DocType
support vector machine, machine learning, medical image analysis, computer-aided detection, dynamic multi-classification and unbalanced data sets
CAD,Vector space,Pattern recognition,Computer science,Classification scheme,Support vector machine,Artificial intelligence,Hyperplane,Clinical decision support system,Virtual colonoscopy,Machine learning,Binary number
Journal
Volume
Issue
Citations 
3
1
17
PageRank 
References 
Authors
1.14
20
4
Name
Order
Citations
PageRank
Mariette Awad110421.39
Yuichi Motai223024.68
Janne Näppi322826.71
Hiroyuki Yoshida46917.19