ArticleJournal of the American Medical Informatics Association : JAMIA
Advancing biomedical image retrieval: development and analysis of a test collection.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Accurate determination of imaging modality using an ensemble of text- and image-based classifiers.Journal of digital imaging · 2012Trial
- Multilingual RECIST classification of radiology reports using supervised learning.Frontiers in digital health · 2023Article
- Towards the creation of a visual ontology of biomedical imaging entities.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Towards case-based medical learning in radiological decision making using content-based image retrieval.BMC medical informatics and decision making · 2011Article
- Biomedical imaging modality classification using combined visual features and textual terms.International journal of biomedical imaging · 2011Article
- Annotation and retrieval of clinically relevant images.International journal of medical informatics · 2009Article
- The ImageCLEFmed medical image retrieval task test collection.Journal of digital imaging · 2009Review
- Effectiveness of Global Features for Automatic Medical Image Classification and Retrieval - the experiences of OHSU at ImageCLEFmed.Pattern recognition letters · 2008Article
- Natural Language Processing Versus Content-Based Image Analysis for Medical Document Retrieval.Journal of the American Society for Information Science and Technology : JASIST · 2008Article
- Effective metadata discovery for dynamic filtering of queries to a radiology image search engine.Journal of digital imaging · 2008Article
- Identifying patient smoking status from medical discharge records.Journal of the American Medical Informatics Association : JAMIAArticle
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6 authors.
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Abstract
objectiveDevelop and analyze results from an image retrieval test collection.
methodsAfter participating research groups obtained and assessed results from their systems in the image retrieval task of Cross-Language Evaluation Forum, we assessed the results for common themes and trends. In addition to overall performance, results were analyzed on the basis of topic categories (those most amenable to visual, textual, or mixed approaches) and run categories (those employing queries entered by automated or manual means as well as those using visual, textual, or mixed indexing and retrieval methods). We also assessed results on the different topics and compared the impact of duplicate relevance judgments.
resultsA total of 13 research groups participated. Analysis was limited to the best run submitted by each group in each run category. The best results were obtained by systems that combined visual and textual methods. There was substantial variation in performance across topics. Systems employing textual methods were more resilient to visually oriented topics than those using visual methods were to textually oriented topics. The primary performance measure of mean average precision (MAP) was not necessarily associated with other measures, including those possibly more pertinent to real users, such as precision at 10 or 30 images.
conclusionsWe developed a test collection amenable to assessing visual and textual methods for image retrieval. Future work must focus on how varying topic and run types affect retrieval performance. Users' studies also are necessary to determine the best measures for evaluating the efficacy of image retrieval systems.
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