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ROC operating point selection for classification of imbalanced data with application to computer-aided polyp
Summary
New operating point selection strategies significantly improve sensitivity and specificity for computer-aided detection (CAD) of colonic polyps. These methods address imbalanced data challenges, offering a robust solution for polyp classification.
Area of Science:
- Medical Imaging
- Machine Learning
- Computational Biology
Background:
- Computer-aided detection and diagnosis (CAD) for colonic polyps struggles with imbalanced datasets, where the optimal classification threshold shifts.
- This threshold shift leads to inferior classification performance in identifying colonic polyps.
Purpose of the Study:
- To propose novel operating point selection strategies based on receiver operating characteristic (ROC) curves to address imbalanced data in colonic polyp CAD.
- To investigate the effectiveness of these new strategies in improving the classification of colonic polyps.
Main Methods:
- Three new operating point selection strategies (shortest distance, harmonic mean, anti-harmonic mean) were developed.
- Experiments utilized a class-imbalanced database of 64 polyps among 786 candidates.
- Support vector machine (SVM) and random forests (RFs) were used as classifiers, with comparisons to cost-sensitive learning and down-sampling techniques.
Main Results:
- The proposed strategies achieved significantly improved and balanced results: approximately 0.89 sensitivity and 0.92 specificity for RFs, and 0.88 sensitivity and 0.90 specificity for SVM.
- These results represent a substantial improvement over the original thresholding methods (e.g., 0.488 sensitivity, 0.986 specificity for RFs).
- The performance of the proposed strategies remained consistent across different levels of data imbalance and was robust regardless of other data correction techniques.
Conclusions:
- The novel operating point selection strategies offer a noticeable gain in sensitivity (improving from ~0.5 to ~0.88-0.89) while maintaining high specificity (~0.90-0.92).
- These strategies provide an adaptive and robust solution to the threshold shifting problem in imbalanced datasets for colonic polyp CAD.
- The findings indicate a feasible approach for achieving favorable sensitivity and specificity in polyp detection systems.
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