Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

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.

[1]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[2]  Joydeep Ghosh,et al.  Generative Oversampling for Mining Imbalanced Datasets , 2007, DMIN.

[3]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[4]  Dazhe Zhao,et al.  Cost sensitive adaptive random subspace ensemble for computer-aided nodule detection , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[5]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[6]  G. R. Sinha,et al.  Abnormality Detection and Classification in Computer-Aided Diagnosis (CAD) of Breast Cancer Images , 2014 .

[7]  Hironori Yamauchi,et al.  Detecting mass and its region in mammograms using mean shift segmentation and Iris Filter , 2010, 2010 10th International Symposium on Communications and Information Technologies.

[8]  Haibo He,et al.  RAMOBoost: Ranked Minority Oversampling in Boosting , 2010, IEEE Transactions on Neural Networks.

[9]  Razvan Pascanu,et al.  Learning Algorithms for the Classification Restricted Boltzmann Machine , 2012, J. Mach. Learn. Res..

[10]  Arturo J. Méndez,et al.  Computerized detection of breast masses in digitized mammograms , 2007, Comput. Biol. Medicine.

[11]  A. Jemal,et al.  Cancer statistics, 2014 , 2014, CA: a cancer journal for clinicians.

[12]  Mawia A. Hassan,et al.  Classification of Breast Tissue as Normal or Abnormal Based on Texture Analysis of Digital Mammogram , 2014 .

[13]  Wei Li,et al.  nsemble-based hybrid probabilistic sampling for imbalanced data earning in lung nodule CAD , 2014 .

[14]  Zhi-Hua Zhou,et al.  Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.