title: Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.
abstract: The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.
authors.affiliation: College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Cao
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.|College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Liu
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.|College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Hang
authors.initials: H
authors.lastname: Bao
authors.affiliation: College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Jinzhu
authors.initials: J
authors.lastname: Yang
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.|College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Dazhe
authors.initials: D
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D000069553:Supervised Machine Learning
keywords: Breast computer aided detection; false-positive reduction; imbalanced data learning; restricted Boltzmann machines; semi-supervised learning