Loshkovska, Suzana
Preferred name
Loshkovska, Suzana
Official Name
Loshkovska, Suzana
Main Affiliation
Email
suzana.loshkovska@finki.ukim.mk
56 results
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Item type:Publication, Rule - Based Model for Medical Knowledge Presentation and Reasoning in Clinical Decision Support Systems(Springer International Publishing, 2016); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Longitudinal Brain MRI Retrieval for Alzheimer’s Disease Using Different Temporal Information(Institute of Electrical and Electronics Engineers (IEEE), 2018); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Image Retrieval for Alzheimer’s Disease Based on Brain Atrophy Pattern(Springer International Publishing, 2017); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Image Representation, Annotation and Retrieval with Predictive Clustering Trees(Springer International Publishing, 2017); ; Džeroski, Sašo - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Internet medical consultant—A knowledge-sharing system(IEEE, 2009-06-22) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fast and scalable image retrieval using predictive clustering trees(Springer, Berlin, Heidelberg, 2013-10-06); ; Džeroski, SashoThe recent overwhelming increase in the amount of available visual information, especially digital images,has brought up a pressing need to develop efficient and accurate systems for image retrieval. Stateof-the-art systems for image retrieval use the bag-of-visual-words representation of the images. However, the computational bottleneck in all such systems is the construction of the visual vocabulary (i.e., how to obtain the visual words). This is typically performed by clustering hundreds of thousands or millions of local descriptors, where the resulting clusters correspond to visual words. Each image is then represented by a histogram of the distribution of its local descriptors throughout the vocabulary. The major issue in the retrieval systems is that by increasing the sizes of the image databases, the number of local descriptors to be clustered increases rapidly: Thus, using conventional clustering techniques is infeasible. Considering this, we propose to construct the visual codebook by using predictive clustering trees, which are very efficient and have good performance. Moreover, to increase the stability of the model, we propose to use random forests of predictive clustering trees. We evaluate the proposed method on a benchmark database of a million images and compare it to other state-of-the-art methods. The results reveal that the proposed method produces a visual vocabulary with superior discriminative power and thus better retrieval performance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Missing Data in Longitudinal Image Retrieval for Alzheimer’s Disease(2022); ; The paper is focused on the missing scans in the context of longitudinal image retrieval for Alzheimer's Disease. Namely, we explore the influence of missing data on the retrieval results when the subjects are represented by the longitudinal changes calculated on the basis of the within-subject template generated using the available time points. To evaluate the effect of the missing scans, we defined two (most characteristic and most common) scenarios, in which missing scans at a specific time point are considered, and one scenario that is based on complete data used as a baseline to compare against. Additionally, we increased the number of patients with missing scans from 10% to 50% and evaluated its impact on the retrieval results. The evaluation showed that from the examined types of feature vectors, concatenated longitudinal changes of the volumes of the cortical and sub-cortical structures are superior and robust. In the case when the dimensionality of the descriptor is an important criterion, we recommend the usage of the percent change or symmetrized percent change of the volumetric measures. Additionally, the influence of the missing scans on the retrieval results is lower when incomplete data occurs in the early time points, rather than in later ones. Moreover, very little or no performance reduction was detected by increasing the number of subjects with missing scans. In general, the evaluation showed very small or no performance degradation in the retrieval process in the scenarios with missing scans, in comparison to the scenario with fully complete data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Information visualization from the public utilities databases of local municipality for municipalities managers(IEEE, 2008-06-23); In this paper, we present the advantages from the use of data visualization from information systems of communal arrangement department for the local selfmanagement. This approach will provide help to the users to analyze and see information more quickly and more efficiently. These visual representations are expected to shorten required time for data analyses. In the paper, we present several visualizations of the integrated data for managers and analytical staff in the local municipality in a city in Republic of Macedonia where the one-stop-shop system is still not implemented. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detection of Visual Concepts and Annotation of Images using Predictive Clustering Trees(2010); ; Djeroski, SashoIn this paper, we present a multiple targets classification system for visual concepts detection and image annotation. Multiple targets classification (MTC) is a variant of classification where an instance may belong to multiple classes at the same time. The system is composed of two parts: feature extraction and classification/annotation. The feature extraction part provides global and local descriptions of the images. These descriptions are then used to learn a classifier and to annotate an image with the corresponding concepts. To this end, we use predictive clustering trees (PCTs), which are capable to classify an instance to multiple classes at once, thus exploit the interactions that may occur among the different visual concepts (classes). Moreover, we constructed ensembles (random forests) of PCTs, to improve the predictive performance. We tested our system on the image database from the visual concept detection and annotation task part of ImageCLEF 2010. The extensive experiments conducted on the benchmark database show that our system has very high predictive performance and can be easily scaled to large number of images and visual concepts. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, (Inductive) Quering environment for predictive clustering trees(2005)