期刊
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
卷 57, 期 -, 页码 4-9出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2016.07.004
关键词
Semi-supervised learning; Deep learning; Computer aided diagnosis; Convolutional neural network; Unlabeled data
资金
- National Institutes of Health [SC1CA166016]
- National Science Foundation [DUE-TUES-1246050]
- Department of Education [P031S120131]
- Border Biological Research Center (BBRC) Core Facilities at The University of Texas at El Paso (UTEP) - RCMI-NIMHD-NIH grant [8G12MD007592]
In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. (C) 2016 Published by Elsevier Ltd.
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