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ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography

  • Bowen Song
  • Guopeng Zhang
  • Wei Zhu
  • Zhengrong Liang
Original Article

Abstract

Purpose

   Computer-aided detection and diagnosis (CAD) of colonic polyps always faces the challenge of classifying imbalanced data. In this paper, three new operating point selection strategies based on receiver operating characteristic curve are proposed to address the problem.

Methods

   Classification on imbalanced data performs inferiorly because of a major reason that the best differentiation threshold shifts due to the degree of data imbalance. To address this decision threshold shifting issue, three operating point selection strategies, i.e., shortest distance, harmonic mean and anti-harmonic mean, are proposed and their performances are investigated.

Results

   Experiments were conducted on a class-imbalanced database, which contains 64 polyps in 786 polyp candidates. Support vector machine (SVM) and random forests (RFs) were employed as basic classifiers. Two imbalanced data correcting techniques, i.e., cost-sensitive learning and training data down sampling, were applied to SVM and RFs, and their performances were compared with the proposed strategies. Comparing to the original thresholding method, i.e., 0.488 sensitivity and 0.986 specificity for RFs and 0.526 sensitivity and 0.977 specificity for SVM, our strategies achieved more balanced results, which are around 0.89 sensitivity and 0.92 specificity for RFs and 0.88 sensitivity and 0.90 specificity for SVM. Meanwhile, their performance remained at the same level regardless of whether other correcting methods are used.

Conclusions

   Based on the above experiments, the gain of our proposed strategies is noticeable: the sensitivity improved from 0.5 to around 0.88 for RFs and 0.89 for SVM while remaining a relatively high level of specificity, i.e., 0.92 for RFs and 0.90 for SVM. The performance of our proposed strategies was adaptive and robust with different levels of imbalanced data. This indicates a feasible solution to the shifting problem for favorable sensitivity and specificity in CAD of polyps from imbalanced data.

Keywords

Computer-aided detection and diagnosis (CAD) Computed tomography colonography (CTC) Random forests Harmonic mean  Support vector machine (SVM) Receiver operating characteristic (ROC) 

Notes

Acknowledgments

This work was supported in part by the NIH/NCI under Grants #CA082402 and #CA143111.

Conflict of Interest

Bowen Song, Guopeng Zhang, Wei Zhu and Zhengrong Liang declare that they have no conflict of interest.

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Copyright information

© CARS 2013

Authors and Affiliations

  • Bowen Song
    • 1
    • 2
  • Guopeng Zhang
    • 3
  • Wei Zhu
    • 2
  • Zhengrong Liang
    • 1
  1. 1.Department of RadiologyStony Brook UniversityStony BrookUSA
  2. 2.Department of Applied Mathematics and StatisticsStony Brook UniversityStony BrookUSA
  3. 3.Department of Biomedical EngineeringFourth Military Medical UniversityXi’anChina

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