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Proceedings Paper

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

Paper Details

Date Published: 20 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350T (20 March 2014); doi: 10.1117/12.2043708
Show Author Affiliations
Wenqing Sun, The Univ. of Texas at El Paso (United States)
Tzu-Liang Tseng, The Univ. of Texas at El Paso (United States)
Bin Zheng, Univ. of Oklahoma (United States)
Flemin Lure, The Univ. of Texas at El Paso (United States)
Teresa Wu, Arizona State Univ. (United States)
Giulio Francia, The Univ. of Texas at El Paso (United States)
Sergio Cabrera, The Univ. of Texas at El Paso (United States)
Jianying Zhang, The Univ. of Texas at El Paso (United States)
Miguel Vélez-Reyesv, The Univ. of Texas at El Paso (United States)
Wei Qian, The Univ. of Texas at El Paso (United States)
Northeastern Univ. (China)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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