1. Research conducted by Peter Stoica and his group in this area is reported in the following papers :

Publications

• Multistatic adaptive microwave imaging for early breast cancer detection. Yao Xie, Bin Guo, Luzhou Xu, Jian Li, and Petre Stoica. In IEEE Transactions on Biomedical Engineering, volume 53, number 8, pp 1647-1657, 2006. (DOI).
• Microwave imaging via adaptive beamforming methods for breast cancer detection. Bin Guo, Yanwei Wang, Jian Li, Peter Stoica, and Renbiao Wu. In J Electromagnetic Waves and Applications, volume 20, pp 53-63, 2006.
• Novel multistatic adaptive microwave imaging methods for early breast cancer detection. Yao Xie, Bin Guo, Jian Li, and Peter Stoica. In Eurasip Journal on Applied Signal Processing, p 91961, 2006. (DOI).
• On multi-static adaptive microwave imaging methods for early breast cancer detection. Yao Xie, Bin Guo, Jian Li, and Peter Stoica. In Proceedings of the 31th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006.
• Multi-static adaptive microwave imaging for early breast cancer detection. Y. Xie, B. Guo, L. Xu, Jian Li, and Peter Stoica. In Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems & Computers, volume 2005 of Conference Record - Asilomar Conference on Signals, Systems and Computers, pp 285-289, 2005. (DOI).
• Microwave imaging via adaptive beamforming methods for breast cancer detection. B. Guo, Y. Wang, Jian Li, and Peter Stoica. In Progress in Electromagnetics Research Symposium, 2005.

2. Research conducted by Kristiaan Pelckmans and coauthors at ESAT - K.U.Leuven, Belgium

Modeling the probabilities of survival for patients after clinical surgery is of primary concern for making accurate diagnosis for those patients. In the following research results we study this modeling task from a theoretical perspective based on contemporary machine learning techniques, and show how those techniques can be used for analyzing survival properties in studies of breast cancer. Here we specifically consider modeling of the relation of survival after surgery and clinical variables on the one hand, and of survival probabilities and micro-array based measurements on the other.

Publications

• Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., Support vector machines for survival analysis, in Proc.of the Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2007), Plymouth, England, Jul. 2007 pdf.
• Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., Survival SVM: a Practical Scalable Algorithm, in Proc. of the 16th European Symposium on Artificial Neural Networks (ESANN2008), Bruges, Belgium, Apr. 2008, pp. 89-94 pdf.

• Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., Additive survival least squares support vector machines, Internal Report 08-136, ESAT-SISTA, K.U.Leuven (Leuven, Belgium), 2008. Accepted for publication in Statistics in Medicine pdf.

• Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints, in Bio-Inspired Systems: Computational and Ambient Intelligence, (Cabestany J., Sandoval F., Prieto A., and and Corchabo J.M., eds.), Proc. of the 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, vol. 5517 of Lecture notes in Computer Science, Springer, 2009, pp. 65-72 pdf.

• Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., MINLIP: Efficient Learning of Transformation Models, Internal Report 09-45, ESAT-SISTA, K.U.Leuven (Leuven, Belgium), 2009. Accepted for publication in Proceedings of the International Conference on Artificial Neural Networks (ICANN2009) pdf.

• Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S., Learning Transformation Models for Ranking and Survival Analysis, Internal Report 09-135, ESAT-SISTA, K.U.Leuven (Leuven, Belgium), 2009 pdf.