Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns

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

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

1 Scopus citations


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 publicationMedical Imaging 2013
Subtitle of host publicationComputer-Aided Diagnosis
StatePublished - Jun 5 2013
EventMedical Imaging 2013: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: Feb 12 2013Feb 14 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherMedical Imaging 2013: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityLake Buena Vista, FL


  • Cluster ensemble
  • Meta clustering

ASJC Scopus subject areas

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


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