Paper Info
Title
Smart Colonography for Distributed Medical Databases with Group Kernel Feature Analysis
Abstract
Computer-Aided Detection (CAD) of polyps in Computed Tomographic (CT) colonography is currently very limited since a single database at each hospital/institution doesn't provide sufficient data for training the CAD system's classification algorithm. To address this limitation, we propose to use multiple databases, (e.g., big data studies) to create multiple institution-wide databases using distributed computing technologies, which we call smart colonography. Smart colonography may be built by a larger colonography database networked through the participation of multiple institutions via distributed computing. The motivation herein is to create a distributed database that increases the detection accuracy of CAD diagnosis by covering many true-positive cases. Colonography data analysis is mutually accessible to increase the availability of resources so that the knowledge of radiologists is enhanced. In this article, we propose a scalable and efficient algorithm called Group Kernel Feature Analysis (GKFA), which can be applied to multiple cancer databases so that the overall performance of CAD is improved. The key idea behind the proposed GKFA method is to allow the feature space to be updated as the training proceeds with more data being fed from other institutions into the algorithm. Experimental results show that GKFA achieves very good classification accuracy.
Year
DOI
Venue
2015
10.1145/2668136
ACM Transactions on Intelligent Systems and Technology
Keywords
DocType
Volume
Algorithms,Computed tomographic colonography,distributed databases,kernel feature analysis,group learning
Journal
6
Issue
ISSN
Citations 
4
2157-6904
1
PageRank 
References 
Authors
0.35
26
4
Name
Order
Citations
PageRank
Yuichi Motai123024.68
Dingkun Ma210.35
Alen Docef3192.78
Hiroyuki Yoshida46917.19