Impact of missing data in training artificial neural networks for computer-aided diagnosis

Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04

Volumn , Issue , 2004, Pages 351-354

  Markey, Mia K ^a   Patel, Amit ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b The University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BREAST CANCER; BREAST LESIONS; CLINICAL DATABASES; LUNG CANCER;

BACKPROPAGATION; BIOPSY; COMPUTER AIDED DIAGNOSIS; LEARNING SYSTEMS; MAMMOGRAPHY; MEDICAL IMAGING; PATHOLOGY; TUMORS;

NEURAL NETWORKS;

EID: 21244439905     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. American Cancer Society, Cancer Facts and Figures 2004. 2004: Atlanta.
2. C.H. Lee, "Screening mammography: proven benefit, continued controversy", Radiologic clinics of North America, 2002, 40. p. 395-407.
3. M.L. Giger, "Computer-aided diagnosis of breast lesions in medical images", Computing in Science & Engineering, 2000, 2(5): p. 39-45.
4. M.L. Giger, N. Karssemeijer, and S.G. Armato, III, "Computer-aided diagnosis in medical imaging", IEEE Transactions on Medical Imaging, 2001, 20(12): p. 1205-1208.
5. C.J. Vyborny, M.L. Giger, and R.M. Nishikawa, "Computer-aided detection and diagnosis of breast cancer", Radiologic Clinics of North America, 2000, 38(4): p. 725-40.
6. Y. Wu, M.L. Giger, K. Doi, C.J. Vyborny, R.A. Schmidt, and C.E. Metz, "Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer", Radiology, 1993, 187(1): p. 81-7.
7. C.E. Kahn, Jr., L.M. Roberts, K.A. Shaffer, and P. Haddawy, "Construction of a Bayesian network for mammographic diagnosis of breast cancer", Computers in Biology & Medicine, 1997, 27(1): p. 19-29.
8. C.E. Floyd, Jr., J.Y. Lo, and G.D. Tourassi, "Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions", American Journal of Roentgenology, 2000, 175(5): p. 1347-52.
9. J.A. Baker, P.J. Kornguth, J.Y. Lo, and C.E. Floyd, Jr., "Artificial neural network: improving the quality of breast biopsy recommendations", Radiology, 1996, 198(1): p. 131-5.
10. G.D. Tourassi, M.K. Markey, J.Y. Lo, and C.E. Floyd, Jr., "A neural network approach to breast cancer diagnosis as a constraint satisfaction problem", Medical Physics, 2001, 28(5): p. 804-11.
11. American College of Radiology, Illustrated breast imaging reporting and data system (BI-RADS™). Third ed. 1998, Reston, VA: American College of Radiology.
12. M.K. Markey, J.Y. Lo, G.D. Tourassi, and C.E. Floyd, Jr., "Self-organizing map for cluster analysis of a breast cancer database", Artificial Intelligence in Medicine., 2003, 27(2): p. 113-27.
13. J.Y. Lo, J.A. Baker, P.J. Kornguth, and C.E. Floyd, Jr., "Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks", Academic Radiology., 1999, 6(1): p. 10-5.
14. T.M. Mitchell, Machine Learning. McGraw-Hill Series in Computer Science, ed C.L. Liu. 1997, Boston: WCB/McGraw-Hill. 414.
15. C.M. Bishop, Neural Networks for Pattern Recognition. 1995, New York: Oxford University Press.
16. C.E. Metz, "Basic principles of ROC analysis", Seminars in Nuclear Medicine, 1978, 8(4): p. 283-98.
17. C.E. Metz, "ROC methodology in radiologic imaging", Investigative Radiology., 1986, 21(9): p. 720-33.