Abstract
This paper describes the medical image retrieval and annotation tasks of ImageCLEF 2006. Both tasks are described with respect to goals, databases, topics, results, and techniques. The ImageCLEFmed retrieval task had 12 participating groups (100 runs). Most runs were automatic, with only a few manual or interactive. Purely textual runs were in the majority compared to purely visual runs but most were mixed, using visual and textual information. None of the manual or interactive techniques were significantly better than automatic runs. The best–performing systems used visual and textual techniques combined, but combinations of visual and textual features often did not improve performance. Purely visual systems only performed well on visual topics. The medical automatic annotation used a larger database of 10,000 training images from 116 classes, up from 9,000 images from 57 classes in 2005. Twelve groups submitted 28 runs. Despite the larger number of classes, results were almost as good as in 2005 which demonstrates a clear improvement in performance. The best system of 2005 would have received a position in the middle in 2006.
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References
Clough, P., Müller, H., Sanderson, M.: Overview of the CLEF cross-language image retrieval track (ImageCLEF) 2004. In: Peters, C., Clough, P.D., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 597–613. Springer, Heidelberg (2005)
Clough, P., Grubinger, M., Deselaers, T., Hanbury, A., Müller, H.: Overview of the ImageCLEF 2006 photographic retrieval and object annotation tasks. In: Proceedings of the CLEF 2006 workshop, Alicante, Spain. LNCS, Springer, Heidelberg (to appear, 2007)
Hersh, W., Müller, H., Jensen, J., Yang, J., Gorman, P., Ruch, P.: ImageCLEFmed: A text collection to advance biomedical image retrieval. Journal of the American Medical Informatics Association, 488–496 (2006)
Deselaers, T., Müller, H., Clogh, P., Ney, H., Lehmann, T.M: The CLEF 2005 automatic medical image annotation task. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, Springer, Heidelberg (2006)
Rosset, A., Müller, H., Martins, M., Dfouni, N., Vallée, J.-P., Ratib, O.: Casimage project – a digital teaching files authoring environment. Journal of Thoracic Imaging 19, 1–6 (2004)
Candler, C.S., Uijtdehaage, S.H., Dennis, S.E.: Introducing HEAL: The health education assets library. Academic Medicine 78, 249–253 (2003)
Wallis, J.W., Miller, M.M., Miller, T.R., Vreeland, T.H.: An internet–based nuclear medicine teaching file. Journal of Nuclear Medicine 36, 1520–1527 (1995)
Glatz–Krieger, K., Glatz, D., Gysel, M., Dittler, M., Mihatsch, M.J.: Webbasierte Lernwerkzeuge für die Pathologie – web–based learning tools for pathology. Pathologe 24, 394–399 (2003)
Hersh, W., Jensen, J., Müller, H., Gorman, P., Ruch, P.: A qualitative task analysis of biomedical image use and retrieval. In: ImageCLEF/MUSCLE workshop on image retrieval evaluation, Vienna, Austria, pp. 11–16 (2005)
Müller, H., Despont–Gros, C., Hersh, W., Jensen, J., Lovis, C., Geissbuhler, A.: Health care professionals’ image use and search behaviour. In: Proceedings of Medical Informatics Europe (MIE 2006), Maastricht, Netherlands, pp. 24–32 (2006)
Müller, H., Geissbuhler, A., Marty, J., Lovis, C., Ruch, P.: The Use of medGIFT and easyIR for Image CLEF 2005. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 724–732. Springer, Heidelberg (2006)
Lehmann, T.M., Schubert, H., Keysers, D., Kohnen, M., Wein, B.B: The IRMA code for unique classification of medical images. In: Proceedings SPIE, vol. 5033, pp. 440–451 (2003)
Keysers, D., Gollan, C., Ney, H.: Classification of medical images using non-linear distortion models. In: Proceedings BVM 2004, Bildverarbeitung für die Medizin, Berlin, Germany, pp. 366–370 (2004)
Deselaers, T., Weyand, T., Keysers, D., Macherey, W., Ney, H.: FIRE in ImageCLEF 2005: Combining content-based image retrieval with textual information retrieval. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 688–698. Springer, Heidelberg (2006)
Kittler, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
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Müller, H., Deselaers, T., Deserno, T., Clough, P., Kim, E., Hersh, W. (2007). Overview of the ImageCLEFmed 2006 Medical Retrieval and Medical Annotation Tasks. In: Peters, C., et al. Evaluation of Multilingual and Multi-modal Information Retrieval. CLEF 2006. Lecture Notes in Computer Science, vol 4730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74999-8_72
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DOI: https://doi.org/10.1007/978-3-540-74999-8_72
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