Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns

Sushravya Raghunath; Srinivasan Rajagopalan; Ronald A. Karwoski; Brian J. Bartholmai; Richard A. Robb

doi:10.1117/12.2008110

18 March 2013 Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns

Sushravya Raghunath, Srinivasan Rajagopalan, Ronald A. Karwoski, Brian J. Bartholmai, Richard A. Robb

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867037 (2013) https://doi.org/10.1117/12.2008110
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Sushravya Raghunath, Srinivasan Rajagopalan, Ronald A. Karwoski, Brian J. Bartholmai, and Richard A. Robb "Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867037 (18 March 2013); https://doi.org/10.1117/12.2008110

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