SWGP: Semi-supervised clustering via Wasserstein generative adversarial network with gradient penalty for uncovering brain disease heterogeneity from medical images

Disease heterogeneity poses a significant challenge for the accurate diagnosis and treatment in brain diseases such as Alzheimer’s disease. Previous attempts to address this problem using machine learning methods have proven to be either insufficiently In order to overcom these limitations, we propose a novel method called Semi-supervised clustering via WGANs with Gradiant Penalty (SWGP) to identify distinct subgroups among patient cohorts through the application of semi-supervised clustering techniques. Essentially, SWGP is a Generative Adversarial Network (GAN) model enhanced with a gradient penalty, consisting of three key modules: the generator, the critic and the clustering. The generator module learns a transformation function by generating pseudo patient data based on healthy individuals, with latent mapping variables to characterize the transformation directions. The critic module evaluates and assigns different scores to the generated pseudo data and the real patient data. Moreover, the clustering module is trained interactively with the generator by assigning patients to their respective subtype memberships. To validate the efficacy of our proposed model, extensive experiments have been conducted on simulated and synthetic datasets. The results highlight that the proposed model outperforms other state-of-the-art solutions in terms of both accuracy and computational efficiency. We further apply our proposal to a publicly available dataset related to Alzheimer’s disease, and demonstrate its potential in capturing biomedical imaging patterns associated with Alzheimer’s disease.