By Topic

Fusion of machine intelligence and human intelligence for colonic polyp detection in CT colonography

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

8 Author(s)
Shijun Wang ; Imaging Biomarkers & Comput.-Aided Diagnosis Lab., Nat. Institutes of Health Clinical Center, Bethesda, MD, USA ; Anugu, V. ; Tan Nguyen ; Rose, N.
more authors

In this paper, we proposed a novel method to improve colonic polyp detection in computed tomographic colonography. Utilizing the human knowledge workers via the Amazon Mechanical Turk (MTurk) webservice, we distributed polyp detections from a computer-aided detection system (CAD) to anonymous online knowledge workers and asked them to distinguish true and false polyp candidates. We combined decisions from the CAD system (machine intelligence) and the MTurk workers (human intelligence) using alpha-integration. Preliminary experimental results indicated that the combined decisions were superior to either alone, with area under the receiver operating characteristic curve improving by 5.8% and 7.0% compared with CAD and MTurk workers alone, respectively.

Published in:

Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on

Date of Conference:

March 30 2011-April 2 2011