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This paper presents a framework for automatically learning shape andappearance models for medical (and certain other) images. It is based on theidea that having a more accurate shape and appearance model leads to moreaccurate image registration, which in turn leads to a more accurate shape andappearance model. This leads naturally to an iterative scheme, which is basedon a probabilistic generative model that is fit using Gauss-Newton updateswithin an EM-like framework. It was developed with the aim of enablingdistributed privacy-preserving analysis of brain image data, such that sharedinformation (shape and appearance basis functions) may be passed across sites,whereas latent variables that encode individual images remain secure withineach site. These latent variables are proposed as features forprivacy-preserving data mining applications.  The approach is demonstrated qualitatively on the KDEF dataset of 2D faceimages, showing that it can align images that traditionally require shape andappearance models trained using manually annotated data (manually definedlandmarks etc.). It is applied to MNIST dataset of handwritten digits to showits potential for machine learning applications, particularly when trainingdata is limited. The model is able to handle ``missing data'', which allows itto be cross-validated according to how well it can predict left-out voxels. Thesuitability of the derived features for classifying individuals into patientgroups was assessed by applying it to a dataset of over 1,900 segmentedT1-weighted MR images, which included images from the COBRE and ABIDE datasets.

An Algorithm for Learning Shape and Appearance Models without Annotations