[
    {
        "id": "osp-25326",
        "type": "article-journal",
        "title": "Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification",
        "author": [
            {
                "family": "Zhang",
                "given": "Shengjie"
            },
            {
                "family": "Zhang",
                "given": "Jinglin"
            },
            {
                "family": "Jiang",
                "given": "Zhuangzhuang"
            },
            {
                "family": "Yu",
                "given": "Ziqi"
            },
            {
                "family": "Zhang",
                "given": "Yipin"
            },
            {
                "family": "Zhang",
                "given": "Qi"
            },
            {
                "family": "Chen",
                "given": "Xiang"
            },
            {
                "family": "Yang",
                "given": "Haibo"
            },
            {
                "family": "Gao",
                "given": "Fei"
            },
            {
                "family": "Cui",
                "given": "Longbiao"
            },
            {
                "family": "Zhou",
                "given": "Yuan"
            },
            {
                "family": "Zhang",
                "given": "Xiao-Yong"
            },
            {
                "family": "Initiative",
                "given": "Alzheimer's Disease Neuroimaging"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/decoupling-disease-covariates-and-individual-variability-a-unified-disentanglement-framework-for-medical-image-classification",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]