A RetroSearch Logo

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Search Query:

Showing content from https://link.springer.com/article/10.1007/s10439-010-0210-4 below:

Improving Breast Cancer Risk Stratification Using Resonance-Frequency Electrical Impedance Spectroscopy Through Fusion of Multiple Classifiers

References
  1. Ali, K. M. and M. J. Pazzani. On the link between error correlation and error reduction in decision tree ensembles. Technical Report ICS-UCI, 1995.

  2. Berg, W. A., J. D. Blume, J. B. Cormack, E. B. Mendelson, et al. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299(18):2151–2163, 2008.

    Article  CAS  PubMed  Google Scholar 

  3. Bilik, I., J. Tabrikian, and A. Cohen. GMM-based target classification for ground surveillance Doppler radar. IEEE Trans. Aerosp. Electron. Syst. 42(1):267–278, 2006.

    Article  Google Scholar 

  4. Bishop, C. M. Neural Networks for Pattern Recognition. Oxford: Oxford University Press, 1995.

    Google Scholar 

  5. Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2:121–167, 1998.

    Article  Google Scholar 

  6. Cao, J., M. Ahmadi, and M. Shridhar. Recognition of handwritten numerals with multiple feature and multistage classifier. Pattern Recognit. 28(2):153–160, 1995.

    Article  Google Scholar 

  7. Chaundhary, S. S., R. K. Mishra, A. Swarup, and J. M. Thomas. Dielectric properties of breast carcinoma and surrounding tissues. IEEE Trans. Biomed. Eng. 35:257–263, 1988.

    Article  Google Scholar 

  8. Cho, S. B., and J. H. Kim. Combining multiple neural networks by fuzzy integral for robust classification. IEEE Trans. Syst. Man Cybern. 25(2):380–384, 1995.

    Article  Google Scholar 

  9. Denisov, D. A., and A. K. Dudkin. Model-based chromosome recognition via hypotheses construction/verification. Pattern Recognit. Lett. 15(3):299–307, 1994.

    Article  Google Scholar 

  10. Fairhurst, M. C., and H. M. S. A. Wahab. An interactive two-level architecture for a memory network pattern classifier. Pattern Recognit. Lett. 10(4):211–215, 1989.

    Article  Google Scholar 

  11. Fenton, J. J., J. Egger, P. A. Carney, G. Cutter, et al. Reality check: perceived versus actual performance of community mammographers. Am. J. Roentgenol. 187:42–46, 2006.

    Article  Google Scholar 

  12. Franco, A., and L. Nanni. Fusion of classifiers for illumination robust face recognition. Expert Syst. Appl. 36:8946–8954, 2009.

    Article  Google Scholar 

  13. Franke, J. and E. Mandler. A comparison of two approaches for combining the votes of cooperating classifiers. In: Proc. 11th IAPR Int’l Conf. Pattern Recognition, Conf. B: Pattern Recognition Methodology and Systems, 1992, pp. 611–614.

  14. Fricke, H., and S. Morse. The electric capacity of tumors of the breast. J. Cancer Res. 16:310–376, 1926.

    Google Scholar 

  15. Glickman, Y. A., O. Filo, U. Nachaliel, S. Lenington, et al. Novel EIS postprocessing algorithm for breast cancer diagnosis. IEEE Trans. Med. Imaging 21:710–712, 2002.

    Article  PubMed  Google Scholar 

  16. Gur, D., B. Zheng, S. Dhurjaty, G. Wolfe, et al. Developing and testing a multi-probe resonance electrical impedance spectroscopy system for detecting breast abnormalities. In: Proc. SPIE, San Diego, 2009, pp. 72631F-1-8.

  17. Gur, D., B. Zheng, D. Lederman, S. Dhurjaty, et al. A support vector machine designed to identify breasts at high risk using multi-probe generated REIS signals: a preliminary assessment. In: Proc. SPIE, San Diego, 2010, pp. 7627B127-46.

  18. Ho, T. K., J. J. Hull, and S. N. Srihari. Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16(1):66–75, 1994.

    Article  Google Scholar 

  19. Holland, J. H. Adaptation in Neural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 1975.

    Google Scholar 

  20. Huang, T. S., and C. Y. Suen. Combining of multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. Pattern Anal. Mach. Intell. 17(1):90–94, 1995.

    Article  Google Scholar 

  21. Kerner, T. E., K. D. Paulsen, A. Hartov, et al. Electrical impedance spectroscopy of the breast: clinical imaging results in 26 subjects. IEEE Trans. Med. Imaging 21:638–645, 2002.

    Article  PubMed  Google Scholar 

  22. Kimura, F., and M. Shridhar. Handwritten numerical recognition based on multiple classifier systems. Pattern Recognit. 24(10):969–983, 1991.

    Article  Google Scholar 

  23. Kitler, J., A. Hojjatoleslami, and T. Windeatt, Weighting factors in multiple expert fusion. In: Proc. British Machine Vision Conf., Colchester, England, 1997, pp. 41–50.

  24. Kittler, J., M. Hatef, R. P. W. Duin, and J. Matas. On combining classifiers. IEEE Trans. PAMI 20(3):226–239, 1998.

    Google Scholar 

  25. Kriege, M., C. T. M. Brekelmans, C. Boetes, P. E. Besnard, et al. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N. Engl. J. Med. 351:427–437, 2004.

    Article  CAS  PubMed  Google Scholar 

  26. Kurzynski, M. W. On the identity of optimal strategies for multiple stage classifiers. Pattern Recognit. Lett. 10(1):39–46, 1989.

    Article  Google Scholar 

  27. Leach, M. O., C. R. Boggis, A. K. Dixon, D. F. Easton, et al. Screening with magnetic resonance imaging and mammography of a UK population at high familiar risk of breast cancer: a prospective multicentre cohort study (MARIBS). Lancet 365:1769–1778, 2005.

    Article  CAS  PubMed  Google Scholar 

  28. Li, H., M. L. Giger, O. I. Olopade, and M. R. Chinander. Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J. Digit. Imaging 21:145–152, 2008.

    Article  PubMed  Google Scholar 

  29. Malich, A., T. Fritsch, R. Anderson, T. Boehm, et al. Electrical impedance scanning for classifying suspicious breast lesions: first results. Eur. Radiol. 10:1555–1561, 2000.

    Article  CAS  PubMed  Google Scholar 

  30. Pipemo, G., G. Frei, and M. Moshitzky. Breast cancer screening by impedance measurement. Med. Biol. Eng. 2:111–117, 1990.

    Google Scholar 

  31. Pisano, E. D., C. Gatsonis, E. Hendrick, M. Yaffe, et al. Diagnostic performance of digital versus film mammography for breast cancer screening. N. Engl. J. Med. 353:1773–1783, 2005.

    Article  CAS  PubMed  Google Scholar 

  32. Poplack, S. P., K. D. Paulsen, A. Hartov, P. M. Meaney, et al. Electromagnetic breast imaging: average tissue property values in women with negative clinical findings. Radiology 231:571–580, 2004.

    Article  PubMed  Google Scholar 

  33. Smith, R. A. Breast cancer screening among women younger than age 50: a current assessment of the issues. CA Cancer J. Clin. 50:312–336, 2000.

    Article  CAS  PubMed  Google Scholar 

  34. Stojadinovic, A., O. Moskovitz, G. Gallimidi, et al. Prospective study of electrical impedance scanning for identifying young women at risk for breast cancer. Breast Cancer Res. Treat. 97:179–189, 2006.

    Article  PubMed  Google Scholar 

  35. Stojadinovic, A., A. Nissan, and Z. Gallimidi. Electrical impedance scanning for the early detection of breast cancer in young women: preliminary results of a multicenter prospective clinical trial. J. Clin. Oncol. 23:2703–2715, 2005.

    Article  PubMed  Google Scholar 

  36. Stojadinovic, A., A. Nissan, and C. D. Shriver. Electrical impedance scanning as a new breast cancer risk stratification tool for young women. J. Surg. Oncol. 97:112–120, 2008.

    Article  PubMed  Google Scholar 

  37. Sumkin, J., B. Zheng, M. Gruss, J. Drescher, et al. Assembling a prototype resonance electrical impedance spectroscopy system for breast tissue signal detection: preliminary assessment. In: Proc. SPIE, 2008, pp. 691716-1-8.

  38. Sumkin, J. H., A. Stojadinovic, and M. Huerbin, Impedance measurements for early detection of breast cancer in younger women: a preliminary assessment. In: Proc. SPIE, 2003, pp. 197–203.

  39. Tang, K. S., K. F. Man, S. Kwong, and Q. H. He. Genetic algorithms and their applications. IEEE Signal Process. Mag. 13(6):22–37, 1996.

    Article  Google Scholar 

  40. Tulyakov, S., S. Jaeger, V. Govindaraju, and D. Doermann. Review of classifier combination methods. In: Studies in Computational Intelligence (SCI), Vol. 90. Berlin, Heidelberg: Springer, 2008, pp. 361–386.

  41. Verbeek, J. J., N. Vlassis, and B. Kröse. Efficient greedy learning of Gaussian mixture models. Neural Comput. 15(2):468–485, 2003.

    Article  Google Scholar 

  42. Vlassis, N., and A. Likas. A greedy EM algorithm for Gaussian mixture learning. Neural Proc. Lett. 15:77–87, 2002.

    Article  Google Scholar 

  43. Warner, E., D. B. Plewes, K. A. Hill, P. A. Causer, et al. Surveillance of BRCA1 and BRCA2 mutation carriers with magnetic resonance imaging, ultrasound, mammography, and clinical breast examination. J. Am. Med. Assoc. 292:1317–1325, 2004.

    Article  CAS  Google Scholar 

  44. WHO, Annual report of the World Health Organization, fact sheet no. 297: Cancer, 2009.

  45. Wolfe, J. N. Breast patterns as an index of risk for developing breast cancer. Am. J. Roentgenol. 126:1130–1139, 1976.

    CAS  Google Scholar 

  46. Wolpert, D. H. Stacked generalization. Neural Netw. 5(2):241–260, 1992.

    Article  Google Scholar 

  47. Xu, L., A. Krzyzak, and C. Y. Suen. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3):418–435, 1992.

    Article  Google Scholar 

  48. Zheng, B., M. L. Zuley, J. H. Sumkin, V. J. Catullo, et al. Detection of breast abnormalities using a prototype resonance electrical impedance spectroscopy system: a preliminary study. Med. Phys. 35:3041–3048, 2008.

    Article  PubMed  Google Scholar 

  49. Zhou, J. Y., and T. Pavlidis. Discrimination of characters by a multi-stage process. Pattern Recognit. 27(11):1539–1549, 1994.

    Article  Google Scholar 

Download references


RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4