Efficient learning by combining confidence-rated classifiers to incorporate unlabeled medical data

Authors:
Weijun He;Xiaolei Huang;Dimitris Metaxas;Xiaoyou Ying
Affiliations:
Center for Computational Biomedicine Imaging and Modeling, Division of Computer and Information Sciences, Rutgers University, NJ;Center for Computational Biomedicine Imaging and Modeling, Division of Computer and Information Sciences, Rutgers University, NJ;Center for Computational Biomedicine Imaging and Modeling, Division of Computer and Information Sciences, Rutgers University, NJ;Sanofi-aventis, Bridgewater, NJ
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabeled data to be hand-labeled and incorporates them into the training set to improve learning performance. This approach has great potential to reduce the training expense in many medical image analysis applications. The main contributions lie in a new strategy to combine confidence-rated classifiers learned on different feature sets and a robust way to evaluate the “informativeness” of each unlabeled example. Our framework is applied to the problem of classifying microscopic cell images. The experimental results show that 1) our strategy is more effective than simply multiplying the predicted probabilities, 2) the error rate of high-confidence predictions is much lower than the average error rate, and 3) hand-labeling informative examples with low-confidence predictions improves performance efficiently and the performance difference from hand-labeling all unlabeled data is very small.