Abstract
The team from the Idiap Research Institute in Martigny, Switzerland, participated in three editions of the CLEF medical image annotation task always reaching among the highest positions in the rankings. Here, we present in detailed form the successful strategies we used in the different editions of the challenge to face the inter– vs. intra–class image variability, to exploit the hierarchical labeling, and to cope with the unbalanced distribution of the classes.
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Tommasi, T., Orabona, F. (2010). Idiap on Medical Image Classification. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds) ImageCLEF. The Information Retrieval Series, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15181-1_24
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DOI: https://doi.org/10.1007/978-3-642-15181-1_24
Publisher Name: Springer, Berlin, Heidelberg
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