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Proceedings Paper

Active relearning for robust supervised classification of pulmonary emphysema
Author(s): Sushravya Raghunath; Srinivasan Rajagopalan; Ronald A. Karwoski; Brian J. Bartholmai; Richard A. Robb
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Paper 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.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152Q (23 February 2012); doi: 10.1117/12.911648
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. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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