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indicating the combined influence of both causes on the probability of having
breast cancer.
References
1. Otten, J.D., Broeders, M.J., Fracheboud, J., Otto, S.J., de Koning, H.J., Verbeek,
A.L.: Impressive time-related influence of the Dutch screening programme on breast
cancer incidence and mortality, 1975-2006. International Journal of Cancer 123(8),
1929-1934 (2008)
2. Engeland, S.V.: Detection of mass lesions in mammograms by using multiple views.
PhD Thesis (2006)
3. BI-RADS: Breast Imaging Reporting and Data System. American College of Ra-
diology, Reston (1993)
4. Morton, M.J., Whaley, D.H., Brandt, K.R., Amrami, K.K.: Screening mammo-
grams: interpretation with computer-aided detection-prospective evaluation. Radi-
ology 239, 375-383 (2006)
5. Sardo, M., Vaidya, S., Jain, L.C.: Advanced Computational Intelligence Paradigms
in Healthcare, vol. 3. Springer, Heidelberg (2008)
6. Buchanan, B.G., Shortliffe, E.H.: Rule-Based Expert Systems: The MYCIN Exper-
iments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading
(1984)
7. Liebowitz, J.: The Handbook of Applied Expert Systems. CRC Press, Boca Raton
(1997)
8. Jain, A., Jain, A., Jain, S., Jain, L.: Artificial Intelligence Techniques in Breast
Cancer Diagnosis and Prognosis. World Scientific, Singapore (2000)
9. Burnside, E.S., Rubin, D.L., Fine, J.P., Shachter, R.D., Sisney, G.A., Leung, W.K.:
Bayesian network to predict breast cancer risk of mammographic microcalcifica-
tions and reduce number of benign biopsy results. Journal of Radiology (2006)
10. Kahn, C.E., Roberts, L.M., Shaffer, K.A., Haddawy, P.: Construction of a bayesian
network for mammographic diagnosis of breast cancer. Computers and Biology and
Medicine 27(1), 19-30 (1997)
11. Karabatak, M., Ince, M.C.: An expert system for detection of breast cancer based
on association rules and neural network. Expert Systems with Applications: An
International Journal 36(2), 3465-3469 (2009)
12. Wolberg, W.H., Street, W.N., Mangasarian, O.L.: Machine learning techniques
to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77, 163-171
(1994)
13. Ye, J., Zheng, S., Yang, C.: SVM-based microcalcification detection in digital mam-
mograms. In: Proceedings of the 2008 International Conference on Computer Sci-
ence and Software Engineering, vol. 6, pp. 89-92 (2008)
14. Roca-Pardinas, J., Cadarso-Suarez, C., Tahoces, P.G., Lado, M.J.: Assessing con-
tinuous bivariate effects among different groups through nonparametric regression
models: An application to breast cancer detection. Computational Statistics &
Data Analysis 52(4), 1958-1970 (2008)
15. Gupta, S., Chyn, P., Markey, M.: Breast cancer CADx based on bi-rads descriptors
from two mammographic views. Medical Physics 33(6), 1810-1817 (2006)
16. Good, W., Zheng, B., Chang, Y., Wang, X., Maitz, G., Gur, D.: Multi-image cad
employing features derived from ipsilateral mammographic views. In: Proceedings
of SPIE, Medical Imaging, vol. 3661, pp. 474-485 (1999)
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