search railabs.duhs.duke.edu.

Papers Published

1. Fischer, E.A. and Lo, J.Y. and Markey, M.K., Bayesian networks of BI-RADS™ descriptors for breast lesion classification, Conference Proceedings. 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.04CH37558), vol. Vol.4 (2004), pp. 3031 - 4 .
   (last updated on 2007/04/15)

   Abstract:
   We investigated Bayesian network structure learning and probability estimation from mammographic feature data in order to classify breast lesions into different pathological categories. We compared the learned networks to naive Bayes classifiers, which are similar to the expert systems previously investigated for breast lesion classification. The learned network structures reflect the difference in the classification of biopsy outcome and the invasiveness of malignant lesions for breast masses and microcalcifications. The difference between masses and microcalcifications should be taken into consideration when interpreting systems for automatic pathological classification of breast lesions. The difference may also affect use of these systems for tasks such as estimating the sampling error of biopsy

   Keywords:
   belief networks;cancer;image classification;learning (artificial intelligence);mammography;Markov processes;medical expert systems;medical image processing;Monte Carlo methods;probability;tumours;