The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Investigation and Reduction of Discretization Variance in Decision Tree Induction
ECML '00 Proceedings of the 11th European Conference on Machine Learning
An empirical investigation of the impact of discretization on common data distributions
Design and application of hybrid intelligent systems
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
Wrapper discretization by means of estimation of distribution algorithms
Intelligent Data Analysis
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
The Journal of Machine Learning Research
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Critiquing knowledge representation in medical image interpretation using structure learning
KR4HC'10 Proceedings of the ECAI 2010 conference on Knowledge representation for health-care
Incorporating expert knowledge when learning Bayesian network structure: A medical case study
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
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Objectives: To obtain a balanced view on the role and place of expert knowledge and learning methods in building Bayesian networks for medical image interpretation. Methods and materials: The interpretation of mammograms was selected as the example medical image interpretation problem. Medical image interpretation has its own common standards and procedures. The impact of these on two complementary methods for Bayesian network construction was explored. Firstly, methods for the discretisation of continuous features were investigated, yielding multinomial distributions that were compared to the original Gaussian probabilistic parameters of the network. Secondly, the structure of a manually constructed Bayesian network was tested by structure learning from image data. The image data used for the research came from screening mammographic examinations of 795 patients, of whom 344 were cancerous. Results: The experimental results show that there is an interesting interplay of machine learning results and background knowledge in medical image interpretation. Networks with discretised data lead to better classification performance (increase in the detected cancers of up to 11.7%), easier interpretation, and a better fit to the data in comparison to the expert-based Bayesian network with Gaussian probabilistic parameters. Gaussian probability distributions are often used in medical image interpretation because of the continuous nature of many of the image features. The structures learnt supported many of the expert-originated relationships but also revealed some novel relationships between the mammographic features. Using discretised features and performing structure learning on the mammographic data has further improved the cancer detection performance of up to 17% compared to the manually constructed Bayesian network model. Conclusion: Finding the right balance between expert knowledge and data-derived knowledge, both at the level of network structure and parameters, is key to using Bayesian networks for medical image interpretation. A balanced approach to building Bayesian networks for image interpretation yields more accurate and understandable Bayesian network models.