Charles E. Metz

According to our database, Charles E. Metz authored at least 17 papers between 1990 and 2010.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2010

A Novel Hybrid Linear/Nonlinear Classifier for Two-Class Classification: Theory, Algorithm, and Applications.

Weijie Chen, Charles E. Metz, Maryellen L. Giger, Karen Drukker

IEEE Trans. Medical Imaging, 2010

2008

Hybrid linear classifier for jointly normal data: theory.

Weijie Chen, Charles E. Metz, Maryellen L. Giger

Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, San Diego, 2008

2007

Optimization of Restricted ROC Surfaces in Three-Class Classification Tasks.

Darrin C. Edwards, Charles E. Metz

IEEE Trans. Medical Imaging, 2007

2006

Three-class ROC analysis-a decision theoretic approach under the ideal observer framework.

Xin He, Charles E. Metz, Benjamin M. W. Tsui, Jonathan M. Links, Eric C. Frey

IEEE Trans. Medical Imaging, 2006

2005

The hypervolume under the ROC hypersurface of "Near-Guessing" and Near-Perfect" observers in N-class classification tasks.

Darrin C. Edwards, Charles E. Metz, Robert M. Nishikawa

IEEE Trans. Medical Imaging, 2005

Restrictions on the three-class ideal observer's decision boundary lines.

Darrin C. Edwards, Charles E. Metz

IEEE Trans. Medical Imaging, 2005

2004

Ideal observers and optimal ROC hypersurfaces in N-class classification.

Darrin C. Edwards, Charles E. Metz, Matthew A. Kupinski

IEEE Trans. Medical Imaging, 2004

Computerized detection and 3-way classification of breast lesions on ultrasound images.

Karen Drukker, Darrin C. Edwards, Maryellen Lissak Giger, Robert M. Nishikawa, Charles E. Metz

Proceedings of the Medical Imaging 2004: Image Processing, 2004

Comparison of two methods of adding jitter to artificial neural network training.

R. M. Zur, Y. Jiang, Charles E. Metz

Proceedings of the CARS 2004. Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and Exhibition, 2004

2001

Ideal Observer Approximation Using Bayesian Classification Neural Networks.

Matthew A. Kupinski, Darrin C. Edwards, Maryellen L. Giger, Charles E. Metz

IEEE Trans. Medical Imaging, 2001

1998

Benefits of Computer-Aided Diagnosis (CAD) in Mammographic Diagnosis of Malignant and Benign Clustered Microcalcifications.

Yulei Jiang, Robert M. Nishikawa, Robert A. Schmidt, Charles E. Metz, Maryellen L. Giger, Kunio Doi

Proceedings of the Digital Mammography, 1998

1995

Analysis of noise properties of a class of exact methods of inverting the 2-D exponential radon transform [SPECT application].

Xiaochuan Pan, Charles E. Metz

IEEE Trans. Medical Imaging, 1995

A unified analysis of exact methods of inverting the 2-D exponential radon transform, with implications for noise control in SPECT.

Charles E. Metz, Xiaochuan Pan

IEEE Trans. Medical Imaging, 1995

1994

Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme.

Yuzheng C. Wu, Kunio Doi, Maryellen L. Giger, Charles E. Metz, Wei Zhang

J. Digit. Imaging, 1994

1993

Simulation studies of data classification by artificial neural networks: Potential applications in medical imaging and decision making.

Yuzheng C. Wu, Kunio Doi, Charles E. Metz, Naoki Asada, Maryellen L. Giger

J. Digit. Imaging, 1993

1990

Investigation of methods for the computerized detection and analysis of mammographic masses.

Maryellen Lissak Giger, Fang-Fang Yin, Kunio Doi, Charles E. Metz, Robert A. Schmidt, Carl J. Vyborny

Proceedings of the Medical Imaging IV: Image Processing, 1990

Image processing and image reconstruction with the use of a-priori information.

Chin-Tu Chen, Valen E. Johnson, Xiaoping Hu, Wing H. Wong, Charles E. Metz

Proceedings of the Medical Imaging IV: Image Processing, 1990