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The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps.(More)
We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results(More)
This paper presents the support for user-defined metrics in the G-PM performance analysis tool. G-PM addresses the demand for aggressive optimisation of Grid applications by using a new approach to performance monitoring. The tool provides developers, integrators, and end users with the ability to analyse the performance characteristics of an application at(More)
Gene regulatory networks model dependencies between genes, and thus they potentially explain normal cell physiology, as well as pathological phenotypes. Because high-throughput technologies for measuring gene expression provide increasingly complete and accurate expression profiles, reverse-engineering of the gene regulatory interactions from observational(More)
We propose a new classification method for the prediction of drug properties, called random feature subset boosting for linear discriminant analysis (LDA). The main novelty of this method is the ability to overcome the problems with constructing ensembles of linear discriminant models based on generalized eigenvectors of covariance matrices. Such linear(More)