Overview of the CLEF 2009 Medical Image Annotation Track

This paper describe the last round of the medical image annotation task in ImageCLEF 2009. After four years, we defined the task as a survey of all the past experience. Seven groups participated to the challenge submitting 19 runs. They were asked to train their algorithms on 12,677 images, labeled according to four different settings representing the yearly annotation tasks, and to classify 1,733 images in the four annotation frameworks. The aim is to understand how each strategy answers to the increasing number of classes and to the unbalancing. A plain classification scheme using support vector machines and local descriptors outperformed the other methods. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Information Search and Retrieval; H.3.4 Systems and Software; H.3.7 Digital Libraries General Terms Measurement, Performance, Experimentation Keywords ImageCLEF, Medical image annotation, Image classification, Sc...

• Type: Journal
• Year: 2009
• Where: CLEF
• Authors: Tatiana Tommasi, Barbara Caputo, Petra Welter, Mark Oliver Güld, Thomas Martin Deserno
• Keywords: Annotation Tasks, CLEF 2009, Image Annotation Task, Information Technology, Medical Image Annotation