Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns. (arXiv:2205.04523v1 [cs.LG])

A plethora of machine learning methods have been applied to imaging data,
enabling the construction of clinically relevant imaging signatures of
neurological and neuropsychiatric diseases. Oftentimes, such methods don't
explicitly model the heterogeneity of disease effects, or approach it via
nonlinear models that are not interpretable. Moreover, unsupervised methods may
parse heterogeneity that is driven by nuisance confounding factors that affect
brain structure or function, rather than heterogeneity relevant to a pathology
of interest. On the other hand, semi-supervised clustering …

arxiv disease gan imaging learning patterns representation representation learning semi-supervised