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In this paper, we describe an approach for the automatic medical annotation task of the 2008 CLEF cross-language image retrieval campaign (ImageCLEF). The data comprise 12076 fully annotated images according to the IRMA code. This work is focused on the process of feature extraction from images and hierarchical multi-label classification. To extract(More)
Predictive models make predictions about values of data using known results from different data, while frequent itemsets describe properties of a subset of the data and are descriptive in nature. In this paper we present a method of building predictive models by using frequency information from frequent itemsets. Modifications were done on three standard(More)
In this paper, we present a hierarchical multi-label classification system for visual concepts detection and image annotation. Hierarchical multi-label classification (HMLC) is a variant of classification where an instance may belong to multiple classes at the same time and these classes/labels are organized in a hierarchy. The system is composed of two(More)
In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local(More)
In this paper a querying environment for analysis of patient clinical data is presented. The data consists of two parts: patients' pathological data and data about corresponding gene expression levels. The querying environment includes a generic algorithm for constructing decision trees, as well as algorithms for discretizing gene expression levels and for(More)
This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in Image-CLEF 2013 medical tasks (modality classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and(More)
The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are(More)