Search Results for author:
Found 7 papers, 5 papers with code
Optimizing contrastive learning for cortical folding pattern detection
Here, we build a self-supervised deep-learning model to detect folding patterns in the cingulate region.
GeoLab: Geometry-based Tractography Parcellation of Superficial White Matter
Superficial white matter (SWM) has been less studied than long-range connections despite being of interest to clinical research, andfew tractography parcellation methods have been adapted to SWM.
Identification of Rare Cortical Folding Patterns using Unsupervised Deep Learning
To this end, we preprocess the brain MR images to focus the learning on the folding morphology and train a beta-VAE to model the inter-individual variability of the folding.
GeoSP: A parallel method for a cortical surface parcellation based on geodesic distance
We present GeoSP, a parallel method that creates a parcellation of the cortical mesh based on a geodesic distance, in order to consider gyri and sulci topology.
Brain Modelling as a Service: The Virtual Brain on EBRAINS
The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science.
Bayesian Inference
Code Generation
Computational Engineering, Finance, and Science
Cryptography and Security
Distributed, Parallel, and Cluster Computing
Neurons and Cognition
Quantitative Methods
Cortical surface parcellation based on intra-subject white matter fiber clustering
The output is the complete labeling of cortical mesh vertices, representing the different cortex sub-parcels, with strong connections to other sub-parcels.
Parallel optimization of fiber bundle segmentation for massive tractography datasets
We present an optimized algorithm that performs automatic classification of white matter fibers based on a multi-subject bundle atlas.
