Case retrieval in medical databases by fusing heterogeneous information.
ABSTRACT A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.
Conference Paper: Depression Diagnosis Based on Ontologies and Bayesian Networks[Show abstract] [Hide abstract]
ABSTRACT: Recently, depression become a general disease in the world due to the promotion of life quality and technology development. Most of people are not aware of the possibility of getting depressed himself in daily life. To accurately diagnose getting depressed becomes an important issue. In this paper, we utilize ontologies and Bayesian networks techniques to build the inference model for inferring the possibility of depression. We propose an ontology model to build the terminology of depression and utilize the Bayesian networks to infer the probability of depression. In addition, the paper also proposes an agent-based platform and addresses the implementation issue. The result shows that it can be well-inferring in the depression diagnosis.Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
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ABSTRACT: To realize the medical semantic annotation of mammogram, a semantic modeling approach for calcifications in mammogram based on hierarchical Bayesian network was proposed. Firstly, support vector machines was used to map low-level image feature into feature semantics, then high-level semantic was captured through feature semantic fusion using Bayesian network, finally semantic model was established. To validate the method, the model was applied to annotate the semantic information of mammograms. In this experiment, we chose 142 images as training set and 50 images as testing set, the results showed that the precision ratio of malignant samples is 81.48%, and benign samples is 73.91%.Strategic Technology (IFOST), 2011 6th International Forum on; 01/2011