Using undiagnosed data to enhance computerized breast cancer analysis with a three stage data labeling method

Wenqing Sun; Tzu-Liang Tseng; Bin Zheng; Flemin Lure; Teresa Wu; Giulio Francia; Sergio Cabrera; Jianying Zhang; Miguel Vélez-Reyesv; Wei Qian

doi:10.1117/12.2043708

20 March 2014 Using undiagnosed data to enhance computerized breast cancer analysis with a three stage data labeling method

Wenqing Sun, Tzu-Liang Tseng, Bin Zheng, Flemin Lure, Teresa Wu, Giulio Francia, Sergio Cabrera, Jianying Zhang, Miguel Vélez-Reyesv, Wei Qian

Author Affiliations +

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90350T (2014) https://doi.org/10.1117/12.2043708
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

A novel three stage Semi-Supervised Learning (SSL) approach is proposed for improving performance of computerized breast cancer analysis with undiagnosed data. These three stages include: (1) Instance selection, which is barely used in SSL or computerized cancer analysis systems, (2) Feature selection and (3) Newly designed ‘Divide Co-training’ data labeling method. 379 suspicious early breast cancer area samples from 121 mammograms were used in our research. Our proposed ‘Divide Co-training’ method is able to generate two classifiers through split original diagnosed dataset (labeled data), and label the undiagnosed data (unlabeled data) when they reached an agreement. The highest AUC (Area Under Curve, also called Az value) using labeled data only was 0.832 and it increased to 0.889 when undiagnosed data were included. The results indicate instance selection module could eliminate untypical data or noise data and enhance the following semi-supervised data labeling performance. Based on analyzing different data sizes, it can be observed that the AUC and accuracy go higher with the increase of either diagnosed data or undiagnosed data, and reach the best improvement (ΔAUC = 0.078, ΔAccuracy = 7.6%) with 40 of labeled data and 300 of unlabeled data.

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Wenqing Sun, Tzu-Liang Tseng, Bin Zheng, Flemin Lure, Teresa Wu, Giulio Francia, Sergio Cabrera, Jianying Zhang, Miguel Vélez-Reyesv, and Wei Qian "Using undiagnosed data to enhance computerized breast cancer analysis with a three stage data labeling method", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350T (20 March 2014); https://doi.org/10.1117/12.2043708

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