Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014 (2014), Article ID 970287, 8 pages
Research Article

A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM

School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China

Received 10 August 2013; Accepted 14 November 2013; Published 9 February 2014

Academic Editors: Y. Lu, J. Shu, and F. Yu

Copyright © 2014 Xin-Sheng Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Xinsheng Zhang, Naining Cao, Hongyan He, and Zhengshan Luo, “Hybrid subspace fusion for microcalcification clusters detection,” Journal of Fiber Bioengineering and Informatics, vol. 8, no. 1, pp. 161–169, 2015. View at Publisher · View at Google Scholar
  • Gwenole Quellec, Mathieu Lamard, Michel Cozic, Gouenou Coatrieux, and Guy Cazuguel, “Multiple-Instance Learning for Anomaly Detection in Digital Mammography,” IEEE Transactions on Medical Imaging, vol. 35, no. 7, pp. 1604–1614, 2016. View at Publisher · View at Google Scholar
  • John Arevalo, Fabio A. Gonzalez, Raul Ramos-Pollan, Jose L. Oliveira, and Miguel Angel Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Computer Methods And Programs In Biomedicine, vol. 127, pp. 248–257, 2016. View at Publisher · View at Google Scholar