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Found 13 papers, 2 papers with code
Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data
The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations.
Source-Free Unsupervised Domain Adaptation: A Survey
Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden.
Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network
In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies.
Learning MRI Artifact Removal With Unpaired Data
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability.
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint
To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs).
Navigator-Free Submillimeter Diffusion Imaging using Multishot-encoded Simultaneous Multi-slice (MUSIUM)
In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach on a 3T MR scanner, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts.
Medical Physics Image and Video Processing
Co-evolution of Functional Brain Network at Multiple Scales during Early Infancy
By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales, where each scale displays the unique reconfiguration pattern in terms of modular organization.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)
Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways.
FRNET: Flattened Residual Network for Infant MRI Skull Stripping
Skull stripping for brain MR images is a basic segmentation task.
Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks
In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images.
Spherical U-Net on Cortical Surfaces: Methods and Applications
In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid.
Statistical Topological Data Analysis - A Kernel Perspective
We consider the problem of statistical computations with persistence diagrams, a summary representation of topological features in data.
