Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013.
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.
['Kalpathy-Cramer|Jayashree|J|', 'de Herrera|Alba García Seco|AG|', 'Demner-Fushman|Dina|D|', 'Antani|Sameer|S|', 'Bedrick|Steven|S|', 'Müller|Henning|H|']
D000465:Algorithms D001185:Artificial Intelligence D004282:Documentation D016247:Information Storage and Retrieval D064847:Multimodal Imaging D009323:Natural Language Processing D010363:Pattern Recognition, Automated D011873:Radiology Information Systems D013382:Subtraction Technique D009626:Terminology as Topic D014584:User-Computer Interface D018875:Vocabulary, Controlled
Image retrieval Biomedical literature Content-based retrieval Multimodal medical retrieval Text-based image retrieval