Abstract
Medical image retrieval could be based on the text describing the image as the caption or the title. The use of text terms to retrieve images have several disadvantages such as term-disambiguation. Recent studies prove that representing text into semantic units (concepts) can improve the semantic representation of textual information. However, the use of conceptual representation has other problems as the miss or erroneous semantic relation between two concepts. Other studies show that combining textual and conceptual text representations leads to better accuracy. Popularly, a score for textual representation and a score for conceptual representation are computed and then a combination function is used to have one score. Although the existing of many combination methods of two scores, we propose in this paper a new combination method based on adaptive version of the genetic algorithm. Experiments are carried out on Medical Information Retrieval Task of the ImageCLEF 2009 and 2010. The results confirm that the combination of both textual and conceptual scores allows best accuracy. In addition, our approach outperforms the other combination methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abderahman, Y.A.: Fusion of similarity measures using genetic algorithm for searching chemical database (2008)
Abdulahhad, K., Chevallet, J., Berrut, C.: Solving concept mismatch through bayesian framework by extending umls meta-thesaurus. In: Conference en Recherche d Infomations et Applications - CORIA 2011, pp. 311–326 (2011)
Aronson, A.R.: Effective mapping of biomedical text to the umls metathesaurus: the metamap program. In: Annual Symposium AMIA, pp. 17–21 (2001)
Aslam, J.A., Montague, M.: Models for metasearch. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 276–284 (2001)
Baziz, M.: Indexation conceptuelle guidée par ontologie pour la recherche d’information. In: Thesis, University Paul Sabatier, Toulouse, France (2005)
Chevallet, J., Lim, J., Le, D.T.H.: Domain knowledge conceptual inter-media indexing: application to multilingual multimedia medical reports. In: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM 2007, pp. 495–504 (2007)
Cimino, J.J., Min, H., Perl, Y.: Consistency across the hierarchies of the umls semantic network and metathesaurus. Journal of Biomedical Informatics 36(6), 450–461 (2003)
Crestani, F.: Exploiting the similarity of non-matching terms at retrieval time. Journal of Information Retrieval 2, 25–45 (1999)
Dinh, D., Tamine, L.: Combining global and local semantic contexts for improving biomedical information retrieval. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 375–386. Springer, Heidelberg (2011)
Fang, H., Zhai, C.: An exploration of axiomatic approaches to information retrieval. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 480–487 (2005)
Fieschi, M., Aronson, A.R., Morky, J.G., Gay, C.W., Humphrey, S.M., Rogers, W.J.: The nlm indexing initiative’s medical text indexer. In: Proceedings of the 11th World Congress on Medical Informatics Demner-Fushman and Lin Answering Clinical Questions, pp. 268–272 (2004)
Gasmi, K., Khemakhem, M., Jemaa, M.B.: A conceptual model for word sense disambiguation in medical image retrieval. In: AIRS, pp. 296–307 (2013)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc. (1989)
Hinrich, J.O.P.: Information retrieval based on word senses (1995)
Hull, D.: Using statistical testing in the evaluation of retrieval experiments. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1993, pp. 329–338 (1993)
Jimeno-Yepes, A.J., McInnes, B.T., Aronson, A.R.: Exploiting mesh indexing in medline to generate a data set for word sense disambiguation. BMC Bioinformatics 12, 223 (2011)
Khan, L., McLeod, D., Hovy, E.: Retrieval effectiveness of an ontology-based model for information selection. VLDB Journal, 71–85 (2004)
Lopez-Herrera, A., Herrera-Viedma, E., Herrera, F.: Applying multi-objective evolutionary algorithms to the automatic learning of extended boolean queries in fuzzy ordinal linguistic information retrieval systems. vol. 160, p. 2192–2205 (2009)
Mihalcea, R., Tarau, P., Figa, E.: Pagerank on semantic networks with application to word sense disambiguation. In: International Conference on Computational Linguistics (COLING), pp. 1126–1132 (2004)
Ogilvie, P., Callan, J.: Combining document representations for known-item search. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 143–150 (2003)
Malo, P., Pyry Siitari, A.S.: Automated query learning with wikipedia and genetic programming 194, 86–110 (2013)
Salton, G.: The smart retrieval system: Experiments in automatic document processing (1970)
Salton, G., McGill, M.: Introduction to modern information retrieval (1983)
Shaw, J.A., Fox, E.A., Shaw, J.A., Fox, E.A.: Combination of multiple searches. In: The Second Text REtrieval Conference (TREC-2), pp. 243–252 (1994)
Ventresque, A., Cazalens, S., Lamarre, P., Valduriez, P.: Improving interoperability using query interpretation in semantic vector spaces. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 539–553. Springer, Heidelberg (2008)
Weeber, M., Mork, J., Aronson, A.: Developping a test collection for biomedical word senes disambiguation. In: Annual Symposium. AMIA Symposium, pp. 746–750 (2001)
Wilcoxon, F.: Individual Comparisons by Ranking Methods. Biometrics Bulletin pp. 80–83 (1945)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Gasmi, K., Torjmen-Khemakhem, M., Tamine, L., Ben Jemaa, M. (2014). A New Combination Method Based on Adaptive Genetic Algorithm for Medical Image Retrieval. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-12844-3_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12843-6
Online ISBN: 978-3-319-12844-3
eBook Packages: Computer ScienceComputer Science (R0)