Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.
Journal:
Bio-medical materials and engineering
Published Date:
Jan 1, 2015
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
Keywords
Algorithms
Breast Neoplasms
Computer Simulation
Data Interpretation, Statistical
False Positive Reactions
Female
Humans
Models, Statistical
Neural Networks, Computer
Pattern Recognition, Automated
Radiographic Image Enhancement
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Sample Size
Sensitivity and Specificity
Stochastic Processes
Supervised Machine Learning