1 citations · 1 across the 11 of their papers we have counts for
11 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…
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…
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…
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,…
A tutorial on multi-view autoencoders using the multi-view-AE library
Ana Lawry Aguila, Andre Altmann
There has been a growing interest in recent years in modelling multiple modalities (or views) of data to for example, understand the relationship between modalities or to generate…
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning
James Chapman, Lennie Wells, Ana Lawry Aguila
The Canonical Correlation Analysis (CCA) family of methods is foundational in multiview learning. Regularised linear CCA methods can be seen to generalise Partial Least Squares (PL…
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…
A Generalized EigenGame with Extensions to Multiview Representation Learning
James Chapman, Ana Lawry Aguila, Lennie Wells
Generalized Eigenvalue Problems (GEPs) encompass a range of interesting dimensionality reduction methods. Development of efficient stochastic approaches to these problems would all…