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Overview of the CLEF 2010 Medical Image Retrieval Track

Overview of the CLEF 2010 Medical Image Retrieval Track,Henning Müller,Jayashree Kalpathy-Cramer,Ivan Eggel,Steven Bedrick,Joe Reisetter,Charles E. Ka

Overview of the CLEF 2010 Medical Image Retrieval Track   (Citations: 4)
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The seventh edition of the ImageCLEF medical retrieval task was organized in 2010. As in 2008 and 2009, the collection in 2010 uses images and captions from the Radiology and Radiographics journals pub- lished by RSNA (Radiological Society of North America). Three sub- tasks were conducted within the auspices of the medical task: modality detection, image-based retrieval and case-based retrieval. The goal of the modality detection task was to detect the acquisition modality of the images in the collection using visual, textual or mixed methods. The goal of the image-based retrieval task was to retrieve an ordered set of images from the collection that best met the information need specified as a textual statement and a set of sample images, while the goal of the case-based retrieval task was to return an ordered set of articles (rather than images) that best met the information need provided as a description of a "case". The number of registrations to the medical task increased to 51 research groups. However, groups submitting runs have remained stable at 16, with the number of submitted runs increasing to 155. Of these, 61 were ad-hoc runs, 48 were case-based runs while the remaining 46 were modal- ity classification runs. The best results for the ad-hoc retrieval topics were obtained using mixed methods with textual methods also performing well. Textual methods were clearly superior for the case-based topics. For the modality de- tection task, although textual and visual methods alone were relatively successful, combining these techniques proved most effective.
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    • ... . These specificities partially explain that traditional visual features perform poorly in this context, especially compared to textual approach [5], calling for new ways to integrate visual features to the retrieval systems...
    • ...Recently, the use of image modality has received a growing interest: a task was dedicated to modality classification during the last ImageCLEF conference [5], and several authors addressed the problem of integrating the predicted modality into a retrieval system [2, 1, 4]. On the one hand, participants in ImageCLEF’s modality classification track only proposed very classical approaches to handle this specific classification problem...
    • ...Although some authors already addressed the issue of automatically determine the modality of an image [4, 1], it was mainly brought to light during the last ImageCLEF conference, which dedicated a track to it [5]...
    • ...Actually, it appears that predicting modality from the accompanying text is more effective than using visual features only [5]...

    Pierre Tirillyet al. On modality classification and its use in text-based image retrieval i...

    • ...The images are classified into one of eight modalities (viz., computerized tomography (CT), graphics (GX), magnetic resonance imaging (MR), X-ray (XR), positron emission tomography (PET), optical imaging (PX), and ultrasound (US)) as defined in the the modality detection task in ImageCLEF 2010 [29]...
    • ...To measure classification performance, we use a test set of 2620 images provided by the ImageCLEFmed’10 organizers [29]...
    • ...The authors would like to thank the CLEF [28], [29] organizers and the Radiological Society of North America (RSNA), for making the database available...

    Sameer K. Antaniet al. A Learning-Based Similarity Fusion and Filtering Approach for Biomedic...

    • ...The collection is a subset of a larger collection of 77,000 images made available by the medical image retrieval track in 2010 [9] of ImageCLEF 1 evaluation...
    • ...We thank the ImageCLEFmed [9] organizers for making the database available for research ...

    Sameer K. Antaniet al. A biomedical image retrieval framework based on classification-driven ...

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