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Learning and adaptive regularisation approaches have become popular in image processing. The complexity of modern imaging tasks, especially those in medical imaging have given rise to the need for more sophisticated, non-standard regularisations, where learning approaches are used to determine the selection of optimal parameters, forward models, data fitting terms or even the regularisation functionals. This minisymposium will bring together researchers with experience in the fields of parameter learning, non-standard adaptive and/or anisotropic approaches and their analysis - not necessarily in the context of regularisation - while particular emphasis will be given on medical imaging applications, e.g. Magnetic Resonance Imaging.