Improving Early Precision in the ImageCLEF Medical Retrieval Task

Bedrick, Steven; Radhouani, Saïd; Kalpathy–Cramer, Jayashree

doi:10.1007/978-3-642-15181-1_21

Cited by 2 publications

(2 citation statements)

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“…Additionally, fourteen case-based topics were provided. Based on research that had demonstrated the improvements in early precision obtained in filtering out images of non-relevant modalities [2], a modality classification sub-task was added in 2010. The goal of this subtask was to classify an image into one of 8 classes (computed tomography, magnetic resonance imaging, nuclear medicine, positron emission tomography, ultrasound, x-ray, optical and graphics).…”

Section: Resultsmentioning

confidence: 99%

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Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Kalpathy–Cramer¹,

Herrera²,

Demner‐Fushman³

et al. 2015

Computerized Medical Imaging and Graphics

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110

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Medical image retrieval and classification have been extremely active research topics over the past 15 years. With the ImageCLEF benchmark in medical image retrieval and classification a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluations campaigns. A detailed analysis of the data also highlights the value of the resources created.

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Section: Resultsmentioning

confidence: 99%

“…Since 2010, some participants have included modality filters (using either text-based or image-based modality detection) in their retrieval approaches [2]. Modality filtration was found to be useful by some participants while others found only minimal benefit using modality.…”

Section: Overview Of Participant Methodsmentioning

confidence: 99%