Search Results for author:
Found 8 papers, 2 papers with code
Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor
Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features.
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.
Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding
Our experimental results show that: 1) the learned embedding vectors can quantitatively encode the commonality and individuality of cortical folding patterns; 2) with the embeddings we can robustly infer the complicated many-to-many anatomical correspondences among different brains and 3) our model can be successfully transferred to new populations with very limited training samples.
Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
Charting cortical growth trajectories is of paramount importance for understanding brain development.
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.
An Auto-Context Deformable Registration Network for Infant Brain MRI
Deformable image registration is fundamental to longitudinal and population analysis.
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.
Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images
Thus, the two trajectories can act as a bridge to link the fixed and the moving images, and guide their registration.
