Text-only Cross-language Image Search at Medical ImageCLEF 2008

We report on simple textual strategies with thesaural resources in order to perform document and query translation for cross-language information retrieval in a collection of annotated medical images. The keystone of our strategy for the previous medical ImageCLEF was to enrich documents and queries with Medical Subject Headings (MeSH) terms extracted from them, in order to translate the more important concepts into an intermediate language. The core technical component of our cross-language search engine is an automatic text categorizer, which associates a set of MeSH terms to any input text, with a top precision at above 90%. Nevertheless, in the new 2008 collection, images are given with more verbose captions, and with an associated article relative to a specific case study. Therefore, our strategy to enrich each document is either to collect MeSH terms from the associated article, either to extract them from the caption. Our results are fair, as we stand on the first part of the participants (0.176 for mean average precision). Nevertheless, it appears that MeSH terms collected from the relative article are not always relevant, as this article can concern a huge set of images in general, and can not to describe precisely the associated image. Moreover, the MeSH terms directly extracted from the captions lead to worst performances, possibly due to the more verbose captions. We try different strategies on weighting scheme or retrieval on articles, but without significant improvements. In conclusion, a mixed strategy to combine the two origins of the MeSH terms should be planned for the next ImageCLEF, while better performances should be obtained in the future by tuning the system with the existing benchmark.