Active relearning for robust supervised classification of pulmonary emphysema

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

Abstract

Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2012
Subtitle of host publicationComputer-Aided Diagnosis
DOIs
StatePublished - Dec 1 2012
EventMedical Imaging 2012: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: Feb 7 2012Feb 9 2012

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8315
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2012: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego, CA
Period2/7/122/9/12

Keywords

  • Active relearning
  • Emphysema
  • HRCT
  • SVM
  • Supervised classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Raghunath, S., Rajagopalan, S., Karwoski, R. A., Bartholmai, B. J., & Robb, R. A. (2012). Active relearning for robust supervised classification of pulmonary emphysema. In Medical Imaging 2012: Computer-Aided Diagnosis [83152Q] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8315). https://doi.org/10.1117/12.911648