UNED-UV at Medical Retrieval Task of ImageCLEF 2011

In this paper, we reported some experiments conducted by our members in the SIG team at the IRIT laboratory in the University of Toulouse within the context of the medical information retrieval (IR) task. As in our previous participation in ImageCLEF, in 2011, our research focuses on the case-based retrieval task. We compared the performance of different state-of-the-art term weighting models for retrieving patient cases that might best suit the clinical information need. Furthermore, we also combined term scores obtained by two state-of-the-art weighting models using a particular data fusion technique. Finally, a state-of-the-art query expansion (QE) technique is used for improving biomedical IR performance.

2011

This paper shows the experimentation and the results obtained for LABERINTO research group at the ImageCLEF 2012 medical task. We focus our work on image retrieval based on textual information related to the image. Last year we demonstrated that query expansion exploiting the hierarchical structure of the MeSH descriptors achieved a significant improvement in image retrieval systems. This year our goal is to improve the results obtained last year adding a relevance factor to the query terms. In addition, we have developed a new strategy combining the expansion strategy based on the hierarchical MeSH structure with another expansion strategy very popular among researchers in this field, where the query terms are expanded using MMTx program. The experiments carried out have shown that a relevance factor for the query terms achieves a significant improvement for the results of the different expansion strategies.

This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in ImageCLEF 2012 medical retrieval task. We investigated by evaluating different weighting models for text retrieval. In the case of the visual retrieval, we focused on extracting low-level features and examining their performance. For, the multimodal retrieval we used late fusion to combine the best text and visual results. We found that the choice of weighting model for text retrieval dramatically influences the outcome of the multimodal retrieval. We tested the multimodal retrieval on data from ImageCLEF 2011 medical task and based on that we submitted new experiments for ImageCLEF 2012. The results show that fusing different modalities in the retrieval can improve the overall retrieval performance.

2005

This article describes the technologies used for the various runs submitted by the University of Geneva in the context of the 2004 ImageCLEF competition. As our expertise is mainly in the field of medical image retrieval, most of our effort was concentrated on the medical image retrieval task. Described are the runs that were submitted including technical details for each of the single runs and a short explication of the obtained results compared with the results of submissions from other groups. We describe the problems encountered with respect to optimising the system and with respect to finding a balance between weighting textual and visual features for retrieval. A better balance seems possible when using training data for optimisation and with relevance judgements being available for a control of the retrieval quality.

2005

This work was part of SUNY at Buffalo's overall participation in cross-language retrieval of image collections (ImageCLEF). Our main goal was to explore the combination of Content-Based Image Retrieval (CBIR) and text retrieval of medical images that have clinical annotations in English, French and German. We used a system that combined the content-based image retrieval system GIFT and the well-known SMART system for text retrieval. Translations of English topics to French were performed by mapping the English text to UMLS concepts using the 2005 UMLS meta-thesaurus. Results show that combining both CBIR and Text retrieval yields significant improvements of retrieval performance.

This paper describes the participation of DAEDALUS at ImageCLEF 2011 Medical Retrieval task. We have focused on multimodal (or mixed) experiments that combine textual and visual retrieval. The main objective of our research has been to evaluate the effect on the medical retrieval process of the existence of an extended corpus that is annotated with the image type, associated to both the image itself and also to its textual description. For this purpose, an image classifier has been developed to tag each document with its class (1st level of the hierarchy: Radiology, Microscopy, Photograph, Graphic, Other) and subclass (2nd level: AN, CT, MR, etc.). For the textual-based experiments, several runs using different semantic expansion techniques have been performed. For the visual-based retrieval, different runs are defined by the corpus used in the retrieval process and the strategy for obtaining the class and/or subclass. The best results are achieved in runs that make use of the image subclass based on the classification of the sample images. Although different multimodal strategies have been submitted, none of them has shown to be able to provide results that are at least comparable to the ones achieved by the textual retrieval alone. We believe that we have been unable to find a metric for the assessment of the relevance of the results provided by the visual and textual processes.

clef.isti.cnr.it

The main goal of this paper is to present our experiments in the classification modality and in the ad-hoc image retrieval tasks with the Medical collection at ImageCLEF 2012 Campaign. This edition we focus on applying new strategies for both the textual and the visual subsystems included in our multimodal retrieval system. The visual subsystem has focus on extending the low-level features vector with concept features. These concept features have been calculated by means of a logistic regression model. The textual subsystem has focus on applying a query reformulation to remove general and domain stop-words, trying to produce a query with only medical-related terms. We have not obtained the results as good as obtained at the Photo annotation retrieval subtask using similar techniques. Therefore, a deep analysis for the Medical collection will be done.

… Working Notes. CLEF …

This article describes the participation of the Image and Text Integration (ITI) group from the United States National Library of Medicine (NLM) in the ImageCLEF 2009 medical retrieval track. Our methods encompass a variety of techniques relating to document ...

This paper presents the 2005 MIRACLE's team participation in the ImageCLEFmed task of ImageCLEF 2005. This task certainly requires the use of image retrieval techniques and therefore it is mainly aimed at image analysis research groups. Although our areas of expertise don't include image analysis research, we decided to make the effort to participate in this task to promote and encourage multidisciplinary participation in all aspects of information retrieval, no matter if it is text or content based. We resort to a publicly available image retrieval system (GIFT [1]) when needed.