Recent advances in machine learning allow to map image features with semantic descriptions into aligned latent representations. These representations are useful in that they capture the "essence" of the observed and described elements, allowing generalization for unseen domains and classes. In addition, they provide the means that allow both: i) to generate new images from arbitrary semantic descriptions and ii) to generate semantic descriptions from input images. In this thesis, we aim at studying the applications of such tools in the medical context. For this purpose, the DXA (dual-energy X-ray Absorptiometry) scans are used. DXAs capture subtle characteristics of patients' body structures which are difficult to notice and analyze by humans but are important for the holistic evaluation of the subject.