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

• Published in: Computerized medical imaging and graphics Vol. 39; pp. 55 - 61
• Main Authors: Kalpathy-Cramer, Jayashree, de Herrera, Alba García Seco, Demner-Fushman, Dina, Antani, Sameer, Bedrick, Steven, Müller, Henning
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2015
• Subjects: Algorithms; Artificial Intelligence; Benchmarking; Biomedical literature; Classification; Content-based retrieval; Documentation - methods; Ground truth; Image retrieval; Information Storage and Retrieval - methods; Internal Medicine; Medical; Multimodal Imaging - methods; Multimodal medical retrieval; Natural Language Processing; Other; Pattern Recognition, Automated - methods; Radiology Information Systems - organization & administration; Retrieval; Subtraction Technique; Tasks; Terminology as Topic; Text-based image retrieval; User-Computer Interface; Vocabulary, Controlled; Text-based image retrieval; Multimodal medical retrieval; Content-based retrieval; Image retrieval; Biomedical literature
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2014.03.004

Abstract 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.