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Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation
Marina Crespo Aguirre, Jonathan Williams-Ramirez, Dina Zemlyanker +13
Neuropathological analyses benefit from spatially precise volumetric reconstructions that enhance anatomical delineation and improve morphometric accuracy. Our prior work has shown…
Generating healthy counterfactuals with denoising diffusion bridge models
Ana Lawry Aguila, Peirong Liu, Marina Crespo Aguirre +1
Generating healthy counterfactuals from pathological images holds significant promise in medical imaging, e.g., in anomaly detection or for application of analysis tools that are d…
Deep generative priors for 3D brain analysis
Ana Lawry Aguila, Dina Zemlyanker, You Cheng +6
Diffusion models have recently emerged as powerful generative models in medical imaging. However, it remains a major challenge to combine these data-driven models with domain knowl…
Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data
Ayodeji Ijishakin, Ana Lawry Aguila, Elizabeth Levitis +3
Combining neuroimaging datasets from multiple sites and scanners can help increase statistical power and thus provide greater insight into subtle neuroanatomical effects. However,…
Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities
Ana Lawry Aguila, James Chapman, Andre Altmann
One of the challenges of studying common neurological disorders is disease heterogeneity including differences in causes, neuroimaging characteristics, comorbidities, or genetic va…