Maurice Samulski

According to our database1, Maurice Samulski authored at least 13 papers between 2007 and 2013.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2013
On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks.
Artif. Intell. Medicine, 2013

2012
A probabilistic framework for image information fusion with an application to mammographic analysis.
Medical Image Anal., 2012

2011
Optimizing Case-Based Detection Performance in a Multiview CAD System for Mammography.
IEEE Trans. Medical Imaging, 2011

Computer aided detection of breast masses in mammography using support vector machine classification.
Proceedings of the Medical Imaging 2011: Computer-Aided Diagnosis, 2011

2010
Discretisation Does Affect the Performance of Bayesian Networks.
Proceedings of the Research and Development in Intelligent Systems XXVII, 2010

Critiquing Knowledge Representation in Medical Image Interpretation Using Structure Learning.
Proceedings of the Knowledge Representation for Health-Care, 2010

2009
Causal Probabilistic Modelling for Two-View Mammographic Analysis.
Proceedings of the Artificial Intelligence in Medicine, 2009

2008
Matching mammographic regions in mediolateral oblique and cranio caudal views: a probabilistic approach.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, San Diego, 2008

An Interactive Computer Aided Decision Support System for Detection of Masses in Mammograms.
Proceedings of the Digital Mammography, 2008

Partially Monotone Networks Applied to Breast Cancer Detection on Mammograms.
Proceedings of the Artificial Neural Networks, 2008

A decision support system for breast cancer detection in screening programs.
Proceedings of the ECAI 2008, 2008

Toward Expert Knowledge Representation for Automatic Breast Cancer Detection.
Proceedings of the Artificial Intelligence: Methodology, 2008

2007
Classification of mammographic masses using support vector machines and Bayesian networks.
Proceedings of the Medical Imaging 2007: Computer-Aided Diagnosis, San Diego, 2007


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