Title: An Interpretable Contrastive GAN Approach for Identifying Heterogeneous Pathological Imaging Patterns
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Neuroimaging, Representation Learning, Contrastive Analysis, GAN, Alzheimer's Disease

Abstract: Despite the rapid development of representation learning applied to neuroimaging, accurately disentangling the heterogeneity of neurological diseases remains a significant challenge. Typically, unsupervised approaches may capture disease heterogeneity that is dominated by confounding factors rather than pathological changes in brain structure or function. Existing semi-supervised methods can reveal disease-specific subtypes or dimensions by contrasting with a background population, but they usually rely on the assumption that non-pathological variations are identically distributed between background and target datasets—a condition often unmet in real-world data. To address this, we introduce InfoSepGAN, a contrastive generative framework designed to separate context (non-pathological) and attribute (pathological) factors between background and target datasets, reducing biases in learned disease-related representations when the assumption is violated. Furthermore, we regularize the learned imaging patterns for continuity, sparsity, and monotonicity, ensuring distinct and interpretable disease-related patterns along each dimension. Finally, InfoSepGAN employs a "synthetic twin" mechanism to perform subject-level counterfactual reconstruction, generating non-pathological counterparts for each patient and providing visualizations of disease-related regions. Experiments on both synthetic and real-world Alzheimer's disease datasets demonstrate that InfoSepGAN effectively extracts pathological imaging patterns while adjusting for potential confounders, outperforming recent baseline methods in both accuracy and interpretability.

TL;DR: InfoSepGAN disentangles heterogeneous pathological imaging patterns and non-pathological variation in neuroimaging, outperforming existing methods.