Critiquing Knowledge Representation in Medical Image Interpretation Using Structure Learning

Medical image interpretation is a difficult problem for which human interpreters, radiologists in this case, are normally better equipped than computers. However, there are many clinical situations where radiologist’s performance is suboptimal, yielding a need for exploitation of computer-based interpretation for assistance. A typical example of such a problem is the interpretation of mammograms for breast-cancer detection. For this paper, we investigated the use of Bayesian networks as a knowledge-representation formalism, where the structure was drafted by hand and the probabilistic parameters learnt from image data. Although this method allowed for explicitly taking into account expert knowledge from radiologists, the performance was suboptimal. We subsequently carried out extensive experiments with Bayesian-network structure learning, for critiquing the Bayesian network. Through these experiments we have gained much insight into the problem of knowledge representation and concluded that structure learning results can be conceptually clear and of help in designing a Bayesian network for medical image interpretation.