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Article
Open AccessMulti-sample \(\zeta \) -mixup: richer, more realistic synthetic samples from a p-series interpolant
Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label inf...
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Chapter and Conference Paper
Prediction of Brain Network Age and Factors of Delayed Maturation in Very Preterm Infants
Babies born very preterm (<32 weeks postmenstral age), are at a high risk of having delayed or altered neurodevelopment. Diffusion MRI (dMRI) is a non-invasive neuroimaging modality that allows for early analy...
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Chapter and Conference Paper
Predictive Subnetwork Extraction with Structural Priors for Infant Connectomes
We present a new method to identify anatomical subnetworks of the human white matter connectome that are predictive of neurodevelopmental outcomes. We employ our method on a dataset of 168 preterm infant conne...
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Chapter and Conference Paper
Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances
We propose a method to identify preterm infants at highest risk of adverse motor function (identified at 18 months of age) using connectome features from a diffusion tensor image (DTI) acquired shortly after b...
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Chapter and Conference Paper
Uncertainty in Tractography via Tract Confidence Regions
Tractography allows us to explore white matter connectivity in diffusion MR images of the brain. However, noise, artifacts and limited resolution introduce uncertainty into the results. We propose a statisti...
