Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1998-07-30
2001-03-27
Davis, George B. (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C706S016000, C706S025000
Reexamination Certificate
active
06208983
ABSTRACT:
The invention is related to expert systems and, more particularly, the invention is a method and apparatus for training and operating a neural network to detect breast cancer from skin potential measurements.
BACKGROUND OF THE DISCLOSURE
A device exists in the prior art that measures a series of breast skin surface potentials for the purpose of detecting breast cancer (See U.S. Pat. Nos. 5,697,369; 5,678,547; 5,660,177; 5,560,357; 5,427,098; 5,320,101; 5,099,844; and 4,955,383, each of which is incorporated herein by reference). In addition to the device for collecting skin surface potential data, the prior art also teaches several techniques for using these skin surface potentials to predict the likelihood of breast cancer. In particular, U.S. Pat. No. 5,697,369 teaches using a neural network to process skin surface potential data to detect cancer in a suspect skin region. However, noise and confounding physiological signals make the training task for a neural network a particular challenge for use in predicting breast cancer.
Various other forms of neural network architectures exist such as those disclosed in Jacobs et al. “Adaptive Mixtures of Local Experts,” Neural Computation, Vol. 3, pp. 79-87 (1991); Waterhouse et al., “Classification Using Hierarchical Mixtures of Experts,” Proc. 1994 IEEE on Neural Networks for Signal Processing IV, pp. 177-186 (1994); and Jordan et al., “Hierarchical Mixtures of Experts and the EM Algorithm,” Neural Computation, Vol. 6, pp. 181-214 (1994), which are hereby incorporated herein by reference.
FIG. 1
depicts a functional block diagram of a two-level hierarchical mixture of experts for a neural network
100
in accordance with the prior art. This architecture uses a plurality of hierarchically arranged expert networks
102
A-
102
D (experts) to classify input data x. Gating networks
104
A and
104
B process the output result from each expert network
102
A-
102
D using a gating parameter g. The gated expert results are then summed (in combiners
106
A and
106
B) at a node of the neural network. The results are then gated by gating network
108
and coupled to the next summing node
110
. In this manner the data (represented as vector x) is used to control both the gates and the experts. Each of the gates apply a weighting values to the expert outputs where the weighting values depend upon the input vector x such that the neural network
100
operates non-linearly. The use of weighted gating forms a network that uses “soft” partitioning of the input space and the expert networks provide local processing within each of the partitions. The soft partitioning network can be trained using an Expectation-Maximization (EM) algorithm.
Heretofore a neural network containing a mixture of experts has not been applied to the complex data set of skin potential data and patient information to detect breast cancer. Therefore, there is a need in the art for an improved method and apparatus for training and operating a neural network to provide an accurate technique for breast cancer detection.
SUMMARY OF THE INVENTION
The present invention is a method and apparatus for training and operating a neural network to detect breast cancer malignancy by analyzing skin surface potential data. In particular, the invention uses certain patient information, such as menstrual cycle information, to “gate” the expert output data into particular populations, i.e., the data is soft partitioned into the populations upon which different expert networks operate. An Expectation-Maximization (EM) routine is used to train the neural network using known patient information, known measured skin potential data and correct diagnoses for the particular training data and patient information. Once trained, the neural network parameters are used in a classifier for predicting breast cancer malignancy when given the patient information and skin potentials of other patients.
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patent: 4955383 (1990-09-01), Faupel
patent: 5099844 (1992-03-01), Faupel
patent: 5320101 (1994-06-01), Faupel
patent: 5427098 (1995-06-01), Faupel
patent: 5560357 (1996-10-01), Faupel
patent: 5660177 (1997-08-01), Faupel
patent: 5678547 (1997-10-01), Faupel
patent: 5697369 (1997-12-01), Long, Jr. et al.
patent: 5983211 (1999-11-01), Heseltine et al.
patent: 6056690 (2000-05-01), Roberts
Polakowski et al, “Computer-Aided Breast Cancer Detection and Diagnosis of Masses Using Difference of Gaussians and Derivative-Based Feature Saliency”, IEEE Transactions on Medical Imaging, Dec. 1997.*
Jordan et al, “Hierarchical Mixtures of Experts and the E.M Algorithm”, IEEE Proceedings of 1993 International Joint Conference on Neural Networks.*
Chen et al, “A Modified Mixtures of Expert Architecture for Classification with Diverse Features”, IEEE International Conference on Neural Networks, Jun. 1997.*
Tam et al., “Integrating Expert Models by Local Receptive Neural Network”, IEEE Proceedings of the International Conference on Intelligent Engineering Systems, Sep. 1997.*
Weigend et al, “Modeling, Learning, and Meaning: Extracting Regimes from Time Series”, IEEE Meditarranean Electro Technical Conference, May 1996.*
Jacobs et al, “Learning Piecewise Control Strategies in a Modular Neural Network Architecture”, IEEE Transactions on System, Man, and Cybernetics, Mar./Apr. 1993.*
Jacobs, Robert A., Jordan, Michael I., Nowlan, Steven J., Hinton, Geoffrey E., “Adaptive Mixtures of Local Experts”, Neural Computation 3, pp. 79-87, 1991.
Jordan, Michael I., Jacobs, Robert A., “Hierarchical Mixtures of Experts and the EM Algorithm”, Neural Computation 6, pp. 181-214, 1994.
Waterhouse, S.R., Robinson, A.J., “Classification using Hierarchical Mixtures of Experts”, Proceedings IEEE Workshop on Neural Networks for Signal Processing IV, pp. 177-186, 1994.
Parra Lucas
Sajda Paul
Spence Clay Douglas
Burke William J.
Davis George B.
Sarnoff Corporation
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