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ASME Press Select Proceedings
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Editor
ISBN-10:
0791802655
No. of Pages:
650
Publisher:
ASME Press
Publication date:
2007
eBook Chapter
12 Advancements in Automated Diagnostic Mammography Using K-PLS Non-Linear Mappings
By
Walker H. Land, Jr.
,
Walker H. Land, Jr.
Dept. Of Bioengineering,
Binghamton University
, Binghamton, NY 13902
.
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Claudia Berman, Jr.
,
Claudia Berman, Jr.
Dept. of Radiology, College of Medicine,
University of South Florida, and H. Lee Moffitt Cancer Center & Research Institute
, Tampa, FL 33612
.
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Thomas Raway, Jr.
,
Thomas Raway, Jr.
Dept. Of Bioengineering,
Binghamton University
, Binghamton, NY 13902
.
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John J. Heine, Jr.
,
John J. Heine, Jr.
H. Lee Moffitt Cancer Center & Research Institute
, Tampa, FL 33612
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Alda Mizaku, Jr.
,
Alda Mizaku, Jr.
Dept. Of Bioengineering,
Binghamton University
, Binghamton, NY 13902
.
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Nataliya Kovalchuk, Jr.
Nataliya Kovalchuk, Jr.
Dept. of Physics,
University of South Florida, and H. Lee Moffitt Cancer Center & Research Institute
, Tampa, FL 33612
.
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Page Count:
6
-
Published:2007
Citation
Land, WH, Jr., Berman, C, Jr., Raway, T, Jr., Heine, JJ, Jr., Mizaku, A, Jr., & Kovalchuk, N, Jr. "Advancements in Automated Diagnostic Mammography Using K-PLS Non-Linear Mappings." Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17. Ed. Dagli, CH. ASME Press, 2007.
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This paper evaluated the contribution of both magnetic resonance mammography (MRM) image and non-image (NI) features (breast cancer risk factors) to benign ∕ malignant diagnostic accuracy using a new data set. This paper focus was using a new Kernealized-Partial Least Squares (K-PLS) and EP-SVM paradigms for this evaluation. An important initial finding is that (only) the non-imag , non-linear mapping region of the feature to diagnostic solution space contains significant diagnostic information, with a resultant overall Az of 0.77. The MRI image features of mass size, mass shape and mass margin yielded an Az of 0.96, which is...
Topics:
Mammography
Abstract
Introduction
Data Description
Kernel Defined Feature Space
Evolutionary Programming
Results
Conclusions
Acknowledgements
References
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