collaborators

7 papers

cs.AI2026

An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing

Lianrui Zuo, Yihao Liu, Gaurav Rudravaram +15

Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends…

eess.IV2026

Evaluation of neuroCombat and deep learning harmonization for multi-site magnetic resonance neuroimaging in youth with prenatal alcohol exposure

Chloe Scholten, Elyssa M. McMaster, Adam M. Saunders +8

In cases of prevalent diseases and disorders, such as Prenatal Alcohol Exposure (PAE), multi-site data collection allows for increased study samples. However, multi-site studies in…

cs.CV2025

MetaVoxel: Joint Diffusion Modeling of Imaging and Clinical Metadata

Yihao Liu, Chenyu Gao, Lianrui Zuo +9

Modern deep learning methods have achieved impressive results across tasks from disease classification, estimating continuous biomarkers, to generating realistic medical images. Mo…

q-bio.NC2025

Characterizing Continuous and Discrete Hybrid Latent Spaces for Structural Connectomes

Gaurav Rudravaram, Lianrui Zuo, Adam M. Saunders +12

Structural connectomes are detailed graphs that map how different brain regions are physically connected, offering critical insight into aging, cognition, and neurodegenerative dis…

q-bio.QM2025

Phenotype discovery of traumatic brain injury segmentations from heterogeneous multi-site data

Adam M. Saunders, Michael E. Kim, Gaurav Rudravaram +8

Traumatic brain injury (TBI) is intrinsically heterogeneous, and typical clinical outcome measures like the Glasgow Coma Scale complicate this diversity. The large variability in s…

cs.CV2025

Self-supervised learning of imaging and clinical signatures using a multimodal joint-embedding predictive architecture

Thomas Z. Li, Aravind R. Krishnan, Lianrui Zuo +5

The development of multimodal models for pulmonary nodule diagnosis is limited by the scarcity of labeled data and the tendency for these models to overfit on the training distribu…