title: Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX, 79968, United States.
authors.forename: Wenqing
authors.initials: W
authors.lastname: Sun
authors.affiliation: Department of Industrial, Manufacturing & Systems Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX, 79968, United States.
authors.forename: Tzu-Liang Bill
authors.initials: TB
authors.lastname: Tseng
authors.affiliation: Department of Biological Sciences, University of Texas at El Paso, 500 West University Avenue, El Paso, TX, 79968, United States; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang, Liaoning, 110819, China.
authors.forename: Jianying
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX, 79968, United States; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang, Liaoning, 110819, China. Electronic address: wqian@utep.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Qian
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D000069553:Supervised Machine Learning
keywords: Computer aided diagnosis; Convolutional neural network; Deep learning; Semi-supervised learning; Unlabeled data