Charles E. Metz focuses on Artificial intelligence, Receiver operating characteristic, Medical imaging, Pattern recognition and Computer vision. His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Standard error and Nuclear medicine. His studies in Machine learning integrate themes in fields like Variety, Data collection, Radiological weapon and Radionuclide imaging.
His studies deal with areas such as Statistical hypothesis testing and Computer-aided diagnosis, Radiology as well as Receiver operating characteristic. He usually deals with Medical imaging and limits it to topics linked to Data mining and Variance components. The various areas that Charles E. Metz examines in his Pattern recognition study include Maximum likelihood, Abdominal computed tomography and Significant difference.
Charles E. Metz mainly focuses on Artificial intelligence, Receiver operating characteristic, Pattern recognition, Computer vision and Mammography. He interconnects Machine learning and Nuclear medicine in the investigation of issues within Artificial intelligence. The concepts of his Receiver operating characteristic study are interwoven with issues in Receiver operating characteristic analysis, Data mining and Medical imaging.
In general Computer vision, his work in Observer and Image restoration is often linked to Biplane linking many areas of study. His Mammography research is multidisciplinary, incorporating elements of Computer-aided diagnosis and Radiology. His work investigates the relationship between Image processing and topics such as Radiography that intersect with problems in Diagnostic accuracy.
The scientist’s investigation covers issues in Receiver operating characteristic, Artificial intelligence, Observer, Computer-aided diagnosis and Statistics. His Receiver operating characteristic study combines topics in areas such as Hypersurface, Data mining and Medical imaging. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Decision theory, Computer vision and Pattern recognition.
In the field of Machine learning, his study on Overfitting overlaps with subjects such as Component. His biological study spans a wide range of topics, including Classifier, Mammography and Medical physics. His study in the field of Bayes' theorem also crosses realms of Interpretation.
His primary scientific interests are in Receiver operating characteristic, Data mining, Observer, Medical imaging and Artificial intelligence. His Receiver operating characteristic study is focused on Statistics in general. The Medical imaging study combines topics in areas such as Curve fitting, Receiver operating characteristic analysis and Observer performance.
His Observer performance research includes themes of Statistical hypothesis testing, Hierarchical database model and Diagnostic accuracy. His Artificial intelligence research is multidisciplinary, relying on both Mammography, Nuclear medicine and Pattern recognition. His Pattern recognition research is multidisciplinary, incorporating elements of Radiographic image interpretation, Screening mammography, BI-RADS and Measure.
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Basic principles of ROC analysis
Charles E. Metz.
Seminars in Nuclear Medicine (1978)
ROC methodology in radiologic imaging
Charles E. Metz.
Investigative Radiology (1986)
Some practical issues of experimental design and data analysis in radiological ROC studies.
Charles E. Metz.
Investigative Radiology (1989)
Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data
Charles E. Metz;Benjamin A. Herman;Jong-Her Shen.
Statistics in Medicine (1998)
Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method.
Donald D. Dorfman;Kevin S. Berbaum;Charles E. Metz.
Investigative Radiology (1992)
Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
Yuzheng Wu;M. L. Giger;Kunio Doi;C. J. Vyborny.
A New Approach for Testing the Significance of Differences Between ROC Curves Measured from Correlated Data
Charles E. Metz;Pu-Lan Wang;Helen B. Kronman.
information processing in medical imaging (1984)
Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.
H P Chan;K Doi;C J Vyborny;R A Schmidt.
Investigative Radiology (1990)
Statistical Comparison of Two ROC-curve Estimates Obtained from Partially-paired Datasets
Charles E. Metz;Benjamin A. Herman;Cheryl A. Roe.
Medical Decision Making (1998)
The exponential Radon transform
Oleh Tretiak;Charles Metz.
Siam Journal on Applied Mathematics (1980)
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