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Proceedings Paper

Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns
Author(s): Sushravya Raghunath; Srinivasan Rajagopalan; Ronald A. Karwoski; Brian J. Bartholmai; Richard A. Robb
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Paper Abstract

Automated lung parenchymal classification usually relies on supervised learning of expert chosen regions representative of the visually differentiable HRCT patterns specific to different pathologies (eg. emphysema, ground glass, honey combing, reticular and normal). Considering the elusiveness of a single most discriminating similarity measure, a plurality of weak learners can be combined to improve the machine learnability. Though a number of quantitative combination strategies exist, their efficacy is data and domain dependent. In this paper, we investigate multiple (N=12) quantitative consensus approaches to combine the clusters obtained with multiple (n=33) probability density-based similarity measures. Our study shows that hypergraph based meta-clustering and probabilistic clustering provides optimal expert-metric agreement.

Paper Details

Date Published: 18 March 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867037 (18 March 2013); doi: 10.1117/12.2008110
Show Author Affiliations
Sushravya Raghunath, Mayo Clinic College of Medicine (United States)
Srinivasan Rajagopalan, Mayo Clinic College of Medicine (United States)
Ronald A. Karwoski, Mayo Clinic College of Medicine (United States)
Brian J. Bartholmai, Mayo Clinic College of Medicine (United States)
Richard A. Robb, Mayo Clinic College of Medicine (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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