activity
20182026
most citedPredicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis

49 citations · 69 across the 16 of their papers we have counts for

collaborators

21 papers

cs.CV2026

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

Shengjie Zhang, Jinglin Zhang, Zhuangzhuang Jiang +10

Accurately isolating disease-related features from confounding covariates (e.g., age, gender, site) and individual variations remains a fundamental challenge in medical image class…

eess.IV2026

Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging

Michael E. Kim, Chenyu Gao, Karthik Ramadass +17

Purpose: Diffusion MRI (dMRI) provides a diverse set of quantitative measures and derived datatypes to assess white matter microstructure and macrostructure. Coupled with the incre…

cs.CV2026

Robust-ComBat: Mitigating Outlier Effects in Diffusion MRI Data Harmonization

Yoan David, Pierre-Marc Jodoin, Alzheimer's Disease Neuroimaging Initiative +1

Harmonization methods such as ComBat and its variants are widely used to mitigate diffusion MRI (dMRI) site-specific biases. However, ComBat assumes that subject distributions exhi…

q-bio.NC2026★ 1 cited

Charting the velocity of brain growth and development

Johanna M. M. Bayer, Augustijn A. A. de Boer, Barbora Rehak-Bucova +30

Brain charts have emerged as a highly useful approach for understanding brain development and aging on the basis of brain imaging and have shown substantial utility in describing t…

q-bio.NC2026

Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease

Samuel D. Anderson, Jordan Jomsky, Nikhil N. Chaudhari +5

Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a powerful framework for quantifying anatomical brain aging. Whereas global BA (GBA) summarizes…

cs.LG2025

Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progression

Houliang Zhou, Rong Zhou, Yangying Liu +6

Identifying objective neuroimaging biomarkers to forecast Alzheimer's disease (AD) progression is crucial for timely intervention. However, this task remains challenging due to the…