Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Volume8670
DOIs
StatePublished - 2013
EventMedical Imaging 2013: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: Feb 12 2013Feb 14 2013

Other

OtherMedical Imaging 2013: Computer-Aided Diagnosis
CountryUnited States
CityLake Buena Vista, FL
Period2/12/132/14/13

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Keywords

  • Cluster ensemble
  • Meta clustering

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Raghunath, S., Rajagopalan, S., Karwoski, R. A., Bartholmai, B. J., & Robb, R. A. (2013). Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 8670). [867037] https://doi.org/10.1117/12.2008110