[2603.04113] Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04113 (cs) [Submitted on 4 Mar 2026] Title:Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast Authors:Mehmet Yigit Avci, Akshit Achara, Andrew King, Jorge Cardoso (and for the Alzheimer's Disease Neuroimaging Initiative) View a PDF of the paper titled Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast, by Mehmet Yigit Avci and 3 other authors View PDF HTML (experimental) Abstract:Demographic attributes such as age, sex, and race can be predicted from medical images, raising concerns about bias in clinical AI systems. In brain MRI, this signal may arise from anatomical variation, acquisition-dependent contrast differences, or both, yet these sources remain entangled in conventional analyses. Without disentangling them, mitigation strategies risk failing to address the underlying causes. We propose a controlled framework based on disentangled representation learning, decomposing brain MRI into anatomy-focused representations that suppress acquisition influence and contrast embeddings that capture acquisition-dependent characteristics. Training predictive models for age, sex, and race on full images, anatomical representations, and contrast-only embeddings allows us to quantify the relative contributions of structure and acquisition to the demographic signal. Across three datasets and multiple MRI seque...