8 papers
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…
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…
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…
CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models
Ana Lawry Aguila, Ayodeji Ijishakin, Juan Eugenio Iglesias +5
Applying machine learning to real-world medical data, e.g. from hospital archives, has the potential to revolutionize disease detection in brain images. However, detecting patholog…
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
Ana Lawry Aguila, Peirong Liu, Oula Puonti +1
Supervised machine learning has enabled accurate pathology detection in brain MRI, but requires training data from diseased subjects that may not be readily available in some scena…
Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization
Peirong Liu, Ana Lawry Aguila, Juan E. Iglesias
Data-driven machine learning has made significant strides in medical image analysis. However, most existing methods are tailored to specific modalities and assume a particular reso…