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

被引：80

作者：

Kalpathy-Cramer, Jayashree ^{[1
,2
]}

de Herrera, Alba Garcia Seco ^{[3
]}

Demner-Fushman, Dina ^{[4
]}

Antani, Sameer ^{[4
]}

Bedrick, Steven ^{[5
]}

Mueller, Henning ^{[3
]}

机构：

[1] Massachusetts Gen Hosp, Athinoula A Martinos Ctr Biomed Imaging, Boston, MA 02114 USA

[2] Harvard Univ, Sch Med, Boston, MA USA

[3] Univ Appl Sci Western Switzerland HES SO, Sierre, Switzerland

[4] Natl Lib Med, NIH, Bethesda, MD USA

[5] Oregon Hlth & Sci Univ, Portland, OR 97201 USA

来源：

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 2015年 / 39卷

关键词：

Multimodal medical retrieval; Image retrieval; Biomedical literature; Content-based retrieval; Text-based image retrieval; FUSION;

D O I：

10.1016/j.compmedimag.2014.03.004

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within 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 evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：55 / 61

页数：7

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