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

Shengjie Zhang; Jinglin Zhang; Zhuangzhuang Jiang; Ziqi Yu; Yipin Zhang; Qi Zhang; Xiang Chen; Haibo Yang; Fei Gao; Longbiao Cui; Yuan Zhou; Xiao-Yong Zhang; Alzheimer's Disease Neuroimaging Initiative

arXiv · 2609.25650

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

Shengjie Zhang·Jinglin Zhang·Zhuangzhuang Jiang·Ziqi Yu·Yipin Zhang·Qi Zhang·Xiang Chen·Haibo Yang·Fei Gao·Longbiao Cui·Yuan Zhou·Xiao-Yong Zhang·Alzheimer's Disease Neuroimaging Initiative

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Abstract

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 covariate-alignment head constrained by cross-subject similarity matching, and a Gaussian head absorbing individual variations. We evaluated our framework across 7 datasets encompassing diverse imaging modalities. MedIDL outperforms state-of-the-art supervised and self-supervised classification methods in accuracy across all datasets. Association analyses demonstrate that MedIDL successfully isolates target-specific latent representations. Gradient-based interpretability mappings localize pathognomonic patterns aligning with established clinical literature.

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Shengjie Zhang, Jinglin Zhang, Zhuangzhuang Jiang, Ziqi Yu, Yipin Zhang, Qi Zhang, Xiang Chen, Haibo Yang, Fei Gao, Longbiao Cui, Yuan Zhou, Xiao-Yong Zhang, Alzheimer's Disease Neuroimaging Initiative. 2026-09-22. Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification. https://arxiv.org/abs/2609.25650

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