"The goal of this project is to develop kernel-based machine learning methods for image classification that employ similarity measures comparing images in a hierarchical fashion - as humans do, but with the accuracy of a computer. These methods shall allow to solve challenging medical imaging problems, in particular they will be applied to the diagnosis of osteoarthritis (OA) and breast cancer, which are ranked among the most burdening diseases.Looking at the visual cortex, it becomes obvious that the human visual system uses `deep' structure consisting of multiple levels of processing operating on more and more abstract representations of the visual scene. This has been successfully copied in computer vision systems, In contrast, kernel-based learning algorithms such as support vector machine (SVM) classifiers mark the state-of-the art in pattern recognition. They employ (Mercer) kernel functions to implicitly define a metric feature space for processing the input data, that is, the kernel defines the similarity between observations, in our case between medical images. Kernel methods are well understood theoretically and give excellent results in practice. However, they are usually considered to be `shallow' learning methods in the sense that they realize only a single layer of non-linear processing. This project will combine hierarchical image processing with the efficiency, theoretical beauty, and accuracy gain of SVMs for advancing the performance of medical imaging systems. This is made possible by marrying the applicants expertise in kernel-based machine learning with the widely recognized knowledge in medical image analysis at his new affiliation The Image Group at the Department of Computer Science, University of Copenhagen (DIKU)."