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Reduction of breast biopsies with a modified self-organizing map

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3 Author(s)
Y. Zheng ; Dept. of Physiol. & Biophys., Mayo Clinic, Rochester, MN, USA ; J. F. Greenleaf ; J. J. Gisvold

A modified self-organizing map with nonlinear weight adjustments has been applied to reduce the number of breast biopsies necessary for breast cancer diagnosis. Tissue features representing texture information from digital sonographic breast images were extracted from sonograms of benign and malignant breast tumors. The resulting hyperspace of data points was then used in a modified self-organizing map that objectively segments population distributions of lesions and accurately establishes benign and malignant regions. These methods were applied to a group of 102 problematic breast cases with sonographic images, including 34 with malignant lesions. All lesions were substantiated by excisional biopsy. The system can isolate clusters of purely benign lesions from other clusters containing both benign and malignant lesions. The hybrid neural network defined a region in which about 60% of the benign lesions were located exclusive of any malignant lesions. The experimental results also suggest that the modified self-organizing map provides more accurate population distribution maps than conventional Kohonen maps

Published in:

IEEE Transactions on Neural Networks  (Volume:8 ,  Issue: 6 )