Learning clinical information from medical images

This lecture will focus on the convergence medical imaging and machine learning techniques for the discovery and quantification of clinically useful information from medical images: The first part of the lecture will describe machine learning techniques such a dictionary learning that can be used for image reconstruction, e.g. the acceleration of MR imaging. The second part will discuss model-based approaches that employ statistical as well as probabilistic approaches for segmentation. In particular, we will focus on atlas-based segmentation approaches that employ advanced machine learning approaches such as manifold learning and classifier fusion to improve the accuracy and robustness of the segmentation approaches.