Learning from imbalanced data: A comparative study for Colon CAD

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 6915, Issue , 2008, Pages

  Yang, Xiaoyun ^a   Zheng, Yalin ^a   Siddique, Musib ^a   Beddoe, Gareth ^a  

^a Medicsight PLC   (United Kingdom)

Author keywords

C4.5; Colonic polyp detection; Over sampling; Support vector machine

Indexed keywords

COMPUTERIZED TOMOGRAPHY; MEDICAL APPLICATIONS; SUPPORT VECTOR MACHINES;

COLONIC POLYP DETECTION; OVER SAMPLING; POLYP SHAPES AND SIZES;

COMPUTER AIDED DIAGNOSIS;

EID: 44349093006     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.770630     Document Type: Conference Paper

References (22)

1. A. Jemal. R. Siegel, E. Ward, T. Murray. J. Xu, Michael, and J. "Thum, "Cancer statistics, 2007," in CA Cancer J Clin, pp. 43-66, 2007.
2. S. Halligan, D. Altman, S. Mallett, S. Taylor, D. Burling, M. Roddie, L. Honeyfield, J. McQuillan, H. Amin, and J. Dehmeshki, "Computed tomographic colonography: Assessment of radiologist performance with and without computer-aided detection," Gastroenterology 131, pp. 1690 1699, December 2006.
3. H. Yoshida and J. Nappi, "Three-dimensional Computer-aided Diagnosis Scheme for Detection of Colonic Polyps," IEEE 'Transactions on Medical imaging 20, pp. 1261 1274, December 2001.
4. R. M. Summers, C. D. Johnson, L. M. Pusanik, J. D. Malley, A. M. Youssef, and J. E. Reed, "Automated polyp detection at CT colonography: Feasibility assessment in a human population," Radiology 219, pp. 51-09, 2001.
5. J. Dehmeshki, H. Amin, W. Wong, M. E. Dehkordi, N. Kamangari, M. Roddie, and J. Costcllo, "Automatic identification of colonic polyp in high-resolution CT images," in Proceedings of SPIE, Medical Imaging 2004: Image Processing, J. M. Fitzpatrick and M. Sonka, eds., 5370, pp. 155-162, 2004.
6. A. K. Jerebko, H. M. Summers, J. D. Malley, M. Franaszek, and C. D. Johnson, "Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees," Medical Physics 30(1), pp. 52 60, 2003.
7. A. Jerebko, J. Malley, M. Franaszek, and R. Summers, "Multiple neural network classification scheme for defection of colonic polyps in CT colonography data sets," Acad Radiol 10(2), pp. 154 60, 2003.
8. A. Jerebko, J. Malley, M. Franaszek, and R. Summers, "Support vector machines committee classification method for computer-aided polyp detection in CT colonography," Acad Radiol 12(4), pp. 479-86, 2005.
9. Y. Zheng, X. Yang, and G. Beddoe, "Reduction of false positives in polyp detection using weighted support vector machines," in Proceedings of the 29th IEEE EMBS Annual International Conference, (Lyon, France), August 23-26 2007.
10. L. Böröczky, L. Zhao, and K. P. Lee, "Feature subset selection for improving the performance of false positive reduction in lung nodule CAD.," Information Technology in Biomedical Engineering, IEEE Transactions on 10, pp. 783 94, July 2006.
11. M. Kubat, R. Holte, and S. Matwin, "Learning when negative examples abound," in ECML '97: Proceedings of the 9th European Conference on Machine Learning, pp. 146-153, Springer-Verlag, (London, UK), 1997.
12. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "Smote: Synthetic minority over-sampling technique," J. Artif. Intell. Res. (JAIR) 16, pp. 321-357, 2002.
13. E. Osuna, R. Freund, and F. Girosi, "Support vector machines: Training and applications," Tech. Rep. ATM-1602, 1997.
14. K. Veropoulos, N. Cristianini, and C. Campbell, "Controlling the sensitivity of support vector machines," in Proceedings of the International Joint Conference, on Artificial Intelligence. (1JCA199), pp. 55 60, (Stockholm, Sweden), 1999.
15. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
16. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning 20(3), pp. 273-297, 1995.
17. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machine and Other Kernel-base Learning Methods, Cambridge University Press. 2000.
18. J. Quinlan, C4-5: Programs for Machine. Learning, Morgan Kaufmann, San Mateo, CA, 1992.
19. R. Akbani. S. Kwek, and N. Japkowicz, "Applying support vector machines to unbalanced datasets," in ECML, pp. 39-50, 2004.
20. Y. Liu, A. An. and X. Huang, "Boosting prediction accuracy on unbalanced datasets with SVM ensembles," in PAKDD, pp. 107 118, 2006.
21. X. Hong, S. Chen, and C. J. Harris, "A kernel-based two-class classifier for unbalanced data sets," Neural Networks, IEEE Transactions on 18, pp. 28 41, Jan 2007.
22. C.-C. Chang and C.-J. Lin, LIBSVM: A Library for Support Vector Machines, 2006.