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A plethora of machine learning methods have been applied to imaging data,enabling the construction of clinically relevant imaging signatures ofneurological and neuropsychiatric diseases. Oftentimes, such methods don'texplicitly model the heterogeneity of disease effects, or approach it vianonlinear models that are not interpretable. Moreover, unsupervised methods mayparse heterogeneity that is driven by nuisance confounding factors that affectbrain structure or function, rather than heterogeneity relevant to a pathologyof interest. On the other hand, semi-supervised clustering methods seek toderive a dichotomous subtype membership, ignoring the truth that diseaseheterogeneity spatially and temporally extends along a continuum. To addressthe aforementioned limitations, herein, we propose a novel method, termedSurreal-GAN (Semi-SUpeRvised ReprEsentAtion Learning via GAN). Usingcross-sectional imaging data, Surreal-GAN dissects underlying disease-relatedheterogeneity under the principle of semi-supervised clustering (clustermappings from normal control to patient), proposes a continuously dimensionalrepresentation, and infers the disease severity of patients at individual levelalong each dimension. The model first learns a transformation function fromnormal control (CN) domain to the patient (PT) domain with latent variablescontrolling transformation directions. An inverse mapping function togetherwith regularization on function continuity, pattern orthogonality andmonotonicity was also imposed to make sure that the transformation functioncaptures necessarily meaningful imaging patterns with clinical significance. Wefirst validated the model through extensive semi-synthetic experiments, andthen demonstrate its potential in capturing biologically plausible imagingpatterns in Alzheimer's disease (AD).

Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns