8 papers
Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling
Gaurav Rudravaram, Lianrui Zuo, Karthik Ramadass +17
Acquisition differences across sites, scanners, and protocols in dMRI introduce variability that complicates structural connectome analysis. This motivates deep learning models tha…
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…
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…
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…
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…
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…