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Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

Accurately isolating disease-related features from confounding covariates (e.g., age, gender, site) and individual variations remains a fundamental challenge in medical image classification. Traditional regression-based approaches may ignore non-linear relations between image features and true covariates. To overcome this issue, we present a generalized Medical Imaging Disentanglement Learning (MedIDL) framework. MedIDL maps image features into three mutually orthogonal latent spaces through specialized disentanglement heads: a disease classification head guided by a supervised loss, a covaria

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