Abstract
We describe the medical image classification task in ImageCLEF 2005–2009. It evolved from a classification task with 57 classes on a total of 10,000 images into a hierarchical classification task with a very large number of potential classes. Here, we describe how the database and the objectives changed over the years and how state–of–the–art approaches from machine learning and computer vision were shown to outperform the nearest neighbor-based classification schemes working on full–image descriptors that were very successful in 2005. In particular the use of discriminative classification methods such as support vector machines and the use of local image descriptors were empirically shown to be important building blocks for medical image classification.
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Tommasi, T., Deselaers, T. (2010). The Medical Image Classification Task. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds) ImageCLEF. The Information Retrieval Series, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15181-1_12
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DOI: https://doi.org/10.1007/978-3-642-15181-1_12
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