What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Internal medicine
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.
His most cited work include:
- Basic principles of ROC analysis (4576 citations)
- ROC methodology in radiologic imaging (1553 citations)
- Some practical issues of experimental design and data analysis in radiological ROC studies. (797 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (41.30%)
- Receiver operating characteristic (28.26%)
- Pattern recognition (16.52%)
What were the highlights of his more recent work (between 2002-2012)?
- Receiver operating characteristic (28.26%)
- Artificial intelligence (41.30%)
- Observer (11.30%)
In recent papers he was focusing on the following fields of study:
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.
Between 2002 and 2012, his most popular works were:
- Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. (223 citations)
- Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. (156 citations)
- Assessment of medical imaging systems and computer aids: a tutorial review. (123 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Internal medicine
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
