Article details

Research area
Natural language & AI

ECDL Workshop of the Cross Language Evaluation Forum, Medical image retrieval track. Corfu, Greece



UNT at ImageCLEFmed 2009


For this year our team participated in the medical image retrieval task. Most of our effort was invested in processing the collection using Metamap to assign Unified Medical Language System (UMLS) concepts to each of the images that included some associated text. This process generated metadata that was added to each image and included the UMLS concept number as well as the primary terms associated to each concept. Queries were also processed using Metamap to generate the corresponding UMLS concepts and terms associated with each query request. The SMART system was used to perform retrieval using a generalized vector space model that included the original text, the automatically assigned UMLS concepts, and the UMLS terms. We use a simple weighting scheme (tf-idf) to perform retrieval. Our text based runs included a simple run and a retrieval feedback run. The parameters for retrieval feedback and for the linear combination of the generalized vector space model were tuned using queries for the 2008 CLEF medical image retrieval task (imageCLEFmed). We also worked on using the results from the open source content-based image retrieval (CBIR) system GIFT but ran into some technical problems that prevented us to generate the retrieval results on time for the deadline. However, the University of Geneva (UG) team allowed us to use one of their Image results. The mixed results were generated using the GIFT run provided by UG and used a standard fusion mechanism by combining the text and CBIR results into a single list. To tune the parameters for the combination we used the results from the imageclefmed 2008 queries. Our results indicate that the pseudo relevance feedback mechanism yields only small improvements. The Combination of image features and text gave mixed results. While the combination of standard retrieval and CBIR yields small improvements, the combination of retrieval feedback and CBIR resulted in results significantly below using only text. At this point we still are investigating the reasons for this unexpected result.

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