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Application of support vector machines to breast cancer screening using mammogram and clinical history data

Publication ,  Journal Article
Land, WH; McKee, D; Velazquez, R; Wong, L; Lo, JY; Anderson, F
Published in: Proceedings of SPIE - The International Society for Optical Engineering
September 15, 2003

The objectives of this paper are to discuss: (1) the development and testing of a new Evolutionary Programming (EP) method to optimally configure Support Vector Machine (SVM) parameters for facilitating the diagnosis of breast cancer; (2) evaluation of EP derived learning machines when the number of BI-RADS™ and clinical history discriminators are reduced from 16 to 7; (3) establishing system performance for several SVM kernels in addition to the EP/Adaptive Boosting (EP/AB) hybrid using the Digital Database for Screening Mammography, University of South Florida (DDSM USF) and Duke data sets; and (4) obtaining a preliminary evaluation of the measurement of SVM learning machine inter-institutional generalization capability using BI-RADS™ data. Measuring performance of the SVM designs and EP/AB hybrid against these objectives will provide quantative evidence that the software packages described can generalize to larger patient data sets from different institutions. Most iterative methods currently in use to optimize learning machine parameters are time consuming processes, which sometimes yield sub-optimal values resulting in performance degradation. SVMs are new machine Intelligence paradigms, which use the Structural Risk Minimization (SRM) concept to develop learning machines. These learning machines can always be trained to provide global minima, given that the machine parameters are optimally computed. In addition, several system performance studies are described which include EP derived SVM performance as a function of: (a) population and generation size as well as a method for generating initial populations and (b) iteratively derived versus EP derived learning machine parameters. Finally, the authors describe a set of experiments providing preliminary evidence that both the EP/AB hybrid and SVM Computer Aided Diagnostic C++ software packages will work across a large population of patients, based on a data set of approximately 2,500 samples from five different institutions.

Duke Scholars

Published In

Proceedings of SPIE - The International Society for Optical Engineering

DOI

ISSN

0277-786X

Publication Date

September 15, 2003

Volume

5032 I

Start / End Page

546 / 556

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4009 Electronics, sensors and digital hardware
  • 4006 Communications engineering
 

Citation

APA
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ICMJE
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Land, W. H., McKee, D., Velazquez, R., Wong, L., Lo, J. Y., & Anderson, F. (2003). Application of support vector machines to breast cancer screening using mammogram and clinical history data. Proceedings of SPIE - The International Society for Optical Engineering, 5032 I, 546–556. https://doi.org/10.1117/12.480235
Land, W. H., D. McKee, R. Velazquez, L. Wong, J. Y. Lo, and F. Anderson. “Application of support vector machines to breast cancer screening using mammogram and clinical history data.” Proceedings of SPIE - The International Society for Optical Engineering 5032 I (September 15, 2003): 546–56. https://doi.org/10.1117/12.480235.
Land WH, McKee D, Velazquez R, Wong L, Lo JY, Anderson F. Application of support vector machines to breast cancer screening using mammogram and clinical history data. Proceedings of SPIE - The International Society for Optical Engineering. 2003 Sep 15;5032 I:546–56.
Land, W. H., et al. “Application of support vector machines to breast cancer screening using mammogram and clinical history data.” Proceedings of SPIE - The International Society for Optical Engineering, vol. 5032 I, Sept. 2003, pp. 546–56. Scopus, doi:10.1117/12.480235.
Land WH, McKee D, Velazquez R, Wong L, Lo JY, Anderson F. Application of support vector machines to breast cancer screening using mammogram and clinical history data. Proceedings of SPIE - The International Society for Optical Engineering. 2003 Sep 15;5032 I:546–556.

Published In

Proceedings of SPIE - The International Society for Optical Engineering

DOI

ISSN

0277-786X

Publication Date

September 15, 2003

Volume

5032 I

Start / End Page

546 / 556

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4009 Electronics, sensors and digital hardware
  • 4006 Communications engineering