ReviewJournal of digital imaging2009
The ImageCLEFmed medical image retrieval task test collection.
Review in Journal of digital imaging, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 59 citations in OpenAlex.
- Revolutionizing endodontics: the impact and innovations of artificial intelligence.BMC oral health · 2026Review
- Searching for scientific evidence in a pandemic: An overview of TREC-COVID.Journal of biomedical informatics · 2021Review
- Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.Frontiers in medicine · 2021Article
- Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2015Review
- Do physicians make their articles readable for their blind or low-vision patients? An analysis of current image processing practices in biomedical journals from the point of view of accessibility.Journal of digital imaging · 2014Article
- Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data.Journal of digital imaging · 2013Review
- Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval.Medical physics · 2012Article
- Towards case-based medical learning in radiological decision making using content-based image retrieval.BMC medical informatics and decision making · 2011Article
- Improving retrieval performance in medical image databases using simulated annealing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2010Article
- Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results.Radiology · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A growing number of clinicians, educators, researchers, and others use digital images in their work and search for them via image retrieval systems. Yet, this area of information retrieval is much less understood and developed than searching for text-based content, such as biomedical literature and its derivations. The goal of the ImageCLEF medical image retrieval task (ImageCLEFmed) is to improve understanding and system capability in search for medical images. In this paper, we describe the development and use of a medical image test collection designed to facilitate research with image retrieval systems and their users. We also provide baseline results with the new collection and describe them in the context of past research with portions of the collection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.