Keywords: Machine Learning for Medical Applications
Abstract
Many medical applications such as registration or tracking can be seen as the optimization of an objective function which involves a data term or similarity measure. Classical similarity measures rely for instance on image intensities, gradients or intensity statistics. In the case of noise or background clutter which is very frequent in the case of medical imaging, they might lead to registration/tracking errors. In this project, we investigate different approches and applications of learning a similarity measure directly from the data, leading to a more robust data term which is adapted to the image characteristics.
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Visit our lab at Garching.
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Please contact Olivier Pauly for available student projects within this research project.