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Alzheimer's Disease Neuroimaging Initiative

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Preprint Sep 2026

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

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 t...

Sheng-Jie Zhang, Jing-Lin Zhang, Zhuang-Zhuang Jiang et al. · 0 citations

Open access Sep 2026

Machine learning-derived Alzheimer’s disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures

Analysis of individual-level summary neuroanatomical signature expression scores revealed that Dimension 1 signature expression correlated strongly with cognitive impairment, clinical severity, functional impairment, and genetic risk, while Dimensional 2 signature expression showed relatively weaker associations.

H. Le, S. Ishrat, B. Yaghooti et al. · medRxiv · 0 citations

Open access Jul 2026

From Fairness Findings to Fairness Claims: An Evidence Classification Scheme for Clinical AI

An evidence classification scheme is introduced that screens for sample size and precision, and integrates stability across design alternatives directly into the fairness claim, on the estimation of the brain-age gap from structural MRI using the Alzheimer's Disease Neuroimaging Initiative data.

D. Stark, K. Ritter, Alzheimer's Disease Neuroimaging Initiative · medRxiv · 0 citations